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This table contains sequences common to BUFR and CREX.
Please consult the original WMO file or printed manual for more details and for possible notes!
00 01 02 03 04 06 07 08 09 10 11 12 13 15 16 18 21
#<PHONE_NUMBER>02 Table A category, line 1 000003 Table A category, line 2 #<PHONE_NUMBER>10 F, part descriptor 000011 X, part descriptor 000012 Y, part descriptor #<PHONE_NUMBER>03 000013 Element name, line 1 000014 Element name, line 2 000015 Units name 000016 Units scale sign 000017 Units scale 000018 Units reference sign 000019 Units reference value 000020 Element data width #<PHONE_NUMBER>03 Table D descriptor to be defined 101000 Delayed replication of 1 descriptor 031001 Delayed descriptor replication factor 000030 Descriptor defining sequence
#<PHONE_NUMBER>01 WMO block number 001002 WMO station number #<PHONE_NUMBER>03 WMO Region number 001004 WMO Region sub-area 001005 Buoy/platform identifier #<PHONE_NUMBER>11 Ship's call sign 001012 Direction of motion of moving observing platform 001013 Speed of motion of moving observing platform #<PHONE_NUMBER>01 Year 004002 Month 004003 Day #<PHONE_NUMBER>04 Hour 004005 Minute #<PHONE_NUMBER>04 Hour 004005 Minute 004006 Second #<PHONE_NUMBER>01 Latitude high accuracy 006001 Longitude #<PHONE_NUMBER>01 Latitude high accuracy 006001 Longitude 007001 Height of station #<PHONE_NUMBER>02 Latitude coarse accuracy 006002 Longitude #<PHONE_NUMBER>02 Latitude coarse accuracy 006002 Longitude 007001 Height of station #<PHONE_NUMBER>23 Latitude and longitude (coarse accuracy) 004003 Day 301012 Time #<PHONE_NUMBER>21 Latitude and longitude (high accuracy)<PHONE_NUMBER>03 (Time period in days)<PHONE_NUMBER>04 (Time period in hours)<PHONE_NUMBER>05 (Time period in minutes) #<PHONE_NUMBER>01 WMO block and station number 002001 Type of station 301011 Date 301012 Time 301022 Latitude and longitude (high accuracy), height of station #<PHONE_NUMBER>01 WMO block and station number 002001 Type of station 301011 Date 301012 Time 301024 Latitude and longitude (coarse accuracy), height of station #<PHONE_NUMBER>05 Buoy/platform identifier 002001 Type of station 301011 Date 301012 Time 301021 Latitude and longitude (high accuracy) #<PHONE_NUMBER>05 Buoy/platform identifier 002001 Type of station 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) #<PHONE_NUMBER>05 Buoy/platform identifier 001012 Direction of motion of moving observing platform 001013 Speed of motion of moving observing platform 002001 Type of station 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) #<PHONE_NUMBER>03 Ship's call sign and motion 002001 Type of station 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) #<PHONE_NUMBER>01 WMO block and station number 002011 Radiosonde type 002012 Radiosonde computational method 301011 Date 301012 Time 301022 Latitude and longitude (high accuracy), height of station #<PHONE_NUMBER>01 WMO block and station number 002011 Radiosonde type 002012 Radiosonde computational method 301011 Date 301012 Time 301024 Latitude and longitude (coarse accuracy), height of station #<PHONE_NUMBER>03 Ship's call sign and motion 002011 Radiosonde type 002012 Radiosonde computational method 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) #<PHONE_NUMBER>03 Ship's call sign and motion 002011 Radiosonde type 002012 Radiosonde computational method 301011 Date 301012 Time 301024 Latitude and longitude (coarse accuracy), height of station #<PHONE_NUMBER>07 Satellite identifier 002021 Satellite instrument data used in processing 002022 Satellite data processing technique used 301011 Date 301012 Time #<PHONE_NUMBER>41 Satellite identifier, data used, and data processing technique; date/time 301021 Latitude, longitude #<PHONE_NUMBER>07 Satellite identifier 002023 Cloud motion computational method 301011 Date 301013 Time 301021 Latitude, longitude #<PHONE_NUMBER>07 Satellite identifier 002024 Integrated mean humidity computational method 301011 Date 301013 Time 301021 Latitude, longitude #<PHONE_NUMBER>11 Year, month, day 301012 Time (hour, minute) 201138 Change width to 16 bits 202131 Change scale to 3 004006 Second 201000 Change width back to Table B 202000 Change scale back to Table B 304030 Location relative to the Earth’s centre 304031 Velocity relative to the Earth’s centre #<PHONE_NUMBER>07 Satellite identifier 001012 Direction of motion of moving observing platform 002048 Satellite sensor indicator 021119 Wind scatterometer geophysical model function 025060 Software identification 202124 Change scale 002026 Cross-track resolution 002027 Along-tract resolution 202000 Change scale back to Table B 005040 Orbit number #<PHONE_NUMBER>07 Satellite identifier 025060 Software identification 001033 Originating/generating centre 001034 Originating/generating sub-centre 001012 Direction of motion of moving observation platform 301045 Satellite location and velocity 002021 Satellite instrument data used in processing 301011 Date (year, month, day) 301012 Time (hour, minute) 201138 Change bit width to 16 bits 202131 Change scale to 3 004006 Second 201000 Change width back to Table B 202000 Change scale back to Table B 301023 Location (latitude, longitude) #<PHONE_NUMBER>04 Antenna polarization 002121 Mean frequency 002113 Number of azimuth looks 002026 Cross-track resolution 002027 Along-track resolution 002111 Radar incidence angle 002140 Satellite radar beam azimuth angle 202127 Change scale to -1 001013 Radar platform velocity 202126 Change scale to -2 007001 Radar platform altitude 202000 Change scale to Table B 025010 Clutter treatment 021064 Clutter noise estimate #<PHONE_NUMBER>11 Radar incidence angle 002112 Radar look angle 021062 Backscatter 021063 Radiometric resolution (Noise value) 021065 Missing packet counter #<PHONE_NUMBER>06 Aircraft identifier 002061 Navigational system 301011 Date 301012 Time 301021 Latitude, longitude 008004 Phase of aircraft flight #<PHONE_NUMBER>05 Buoy/platform identifier 002001 Type of station 301011 Date 301012 Time 301021 Latitude and longitude (high accuracy) 001012 Direction of motion of moving observing platform 001014 Platform drift speed (high precision) #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Replication factor 301001 WMO block and station number #<PHONE_NUMBER>06 Aircraft flight number (see Note) 001008 Aircraft registration number (see Note) 002001 Type of station 002002 Type of instrumentation for wind measurement 002005 Precision of temperature observation 002062 Type of aircraft data relay system 002070 Original specification of latitude/longitude 002065 ACARS ground receiving station #<PHONE_NUMBER>11 Year, month, day 301013 Hour, minute, second 301023 Latitude and longitude (coarse accuracy) 007004 Pressure 002064 Aircraft roll angle quality 008004 Phase of aircraft flight #<PHONE_NUMBER>07 Satellite identifier 001031 Generating centre 002020 Satellite classification 002028 Segment size at nadir in X direction 002029 Segment size at nadir in Y direction #<PHONE_NUMBER>71 Satellite identification, Generation resolution 301011 Date 301013 Time 301021 Latitude, longitude
#<PHONE_NUMBER>04 Pressure (at station level) 010051 Pressure reduced to mean sea level 010061 3-hour pressure change 010063 Characteristic of pressure tendency #<PHONE_NUMBER>04 Pressure (at station level) 007004 Pressure level 010003 Geopotential of pressure level 010061 3-hour pressure change 010063 Characteristic of pressure tendency #<PHONE_NUMBER>11 Wind direction (10 m) 011012 Wind speed (10 m) 012004 Temperature (2 m) 012006 Dew point (2 m) 013003 Relative humidity 020001 Horizontal visibility 020003 Present weather 020004 Past weather (1) 020005 Past weather (2) #<PHONE_NUMBER>10 Cloud cover (total) 008002 Vertical significance 020011 Cloud amount 020013 Height of base of cloud 020012 Cloud type 020012 Cloud type 020012 Cloud type #<PHONE_NUMBER>02 Vertical significance 020011 Cloud amount 020012 Cloud type 020013 Height of base of cloud #<PHONE_NUMBER>04 Pressure (at station level) 010051 Pressure reduced to mean sea level 010062 24-hour pressure change 010063 Characteristic of pressure tendency #<PHONE_NUMBER>01 Pressure and pressure change 302003 Wind, temperature, humidity, visibility, weather 302004 Significant cloud layer #<PHONE_NUMBER>02 Pressure and pressure change 302003 Wind, temperature, humidity, visibility, weather 302004 Significant cloud information #<PHONE_NUMBER>06 Pressure and pressure change 302003 Wind, temperature, humidity, visibility, weather 101000 Delayed replication of 1 descriptor 031001 Delayed descriptor replication factor 302005 Cloud layer information #<PHONE_NUMBER>01 Direction of waves 022011 Period of waves 022021 Height of waves #<PHONE_NUMBER>02 Direction of wind waves 022012 Period of wind waves 022022 Height of wind waves #<PHONE_NUMBER>03 Direction of swell waves 022013 Period of swell waves 022023 Height of swell waves #<PHONE_NUMBER>22 Wind waves 101002 Replicate 1 descriptor 2 times 302023 Swell waves (2 systems of swell) #<PHONE_NUMBER>04 Pressure 010051 Pressure reduced to mean sea level 007004 Pressure (vertical location) 010003 Geopotential 012004 Dry-bulb temperature at 2 m 012051 Standard deviation temperature 012016 Maximum temperature at 2 m, past 24 hours 012017 Minimum temperature at 2 m, past 24 hours 013004 Vapour pressure 102004 Replicate 2 descriptors 4 times 008051 Qualifier for number of missing values in calculation of statistic 008020 Total number of missing entities (with respect to accumulation or average)
#<PHONE_NUMBER>03 Geopotential 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure 010003 Geopotential 012001 Temperature 012003 Dew point #<PHONE_NUMBER>04 Pressure 010003 Geopotential 012001 Temperature 012003 Dew point 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>03 Geopotential 008001 Vertical sounding significance 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure 008001 Vertical sounding significance 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure 008001 Vertical sounding significance 010003 Geopotential 012001 Temperature 013003 Relative humidity 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure 008001 Vertical sounding significance 010003 Geopotential 012001 Temperature 012003 Dew point 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>04 Pressure (1) 007004 Pressure (2) defines layer 204007 Add associated field of 7 bits 031021 Additional field significance #<PHONE_NUMBER>21 Layer, quality 010003 Geopotential (layer mean thickness) 204000 Cancel the added associated field #<PHONE_NUMBER>21 Layer, quality 012001 Temperature (layer mean) 204000 Cancel the added associated field #<PHONE_NUMBER>21 Layer, quality 013016 Precipitation water 204000 Cancel the added associated field #<PHONE_NUMBER>25 Satellite channel 204007 Add associated field of 7 bits 031021 Additional field significance 012063 Brightness temperature 204000 Cancel the added associated field #<PHONE_NUMBER>04 Pressure 008003 Vertical significance 204007 Add associated field of 7 bits 031021 Additional field significance 012001 Temperature 204000 Cancel the added associated field #<PHONE_NUMBER>04 Pressure 204007 Add associated field of 7 bits 031021 Additional field significance 010003 Geopotential 204000 Cancel the added associated field #<PHONE_NUMBER>04 Pressure 008003 Vertical significance (base of sounding) 007021 Elevation (local zenith) 007022 Solar elevation (solar zenith) 008012 Land/sea qualifier 012061 Skin temperature #<PHONE_NUMBER>11 Cloud amount 020016 Pressure at top of cloud #<PHONE_NUMBER>10 Cloud cover (total) 020016 Pressure at the top of cloud #<PHONE_NUMBER>52 Geostationary satellite instrument used 002023 Cloud motion computational method 007004 Pressure 011001 Wind direction 011002 Wind speed 002153 Satellite channel centre frequency 002154 Satellite channel band width 012071 Coldest cluster T
#<PHONE_NUMBER>03 Vertical significance 010004 Pressure 012001 Temperature 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>03 Vertical significance 010004 Pressure 011001 Wind direction 011002 Wind speed #<PHONE_NUMBER>03 Vertical significance 012001 Temperature #<PHONE_NUMBER>03 Vertical significance 010004 Pressure 020010 Cloud cover (total) 012001 Temperature #<PHONE_NUMBER>24 Integrated mean humidity computational method 007004 Pressure(1) 007004 Pressure (2) defines layer 013003 Relative humidity #<PHONE_NUMBER>01 Outgoing long-wave radiation 014001 Incoming long-wave radiation 014003 Outgoing short-wave radiation #<PHONE_NUMBER>63 Height assignment method 002164 Tracer correlation method 008012 Land/sea qualifier 007024 Satellite zenith angle 002057 Origin of first guess information 008021 Time significance 004001 Year 004002 Month 004003 Day 004004 Hour 008021 Time significance 004024 Time period or displacement 110004 Replicate 10 descriptors 4 times 008021 Time significance 004004 Hour 004005 Minute 004006 Second 008021 Time significance 004004 Hour 004005 Minute 004006 Second 011001 Wind direction 011002 Wind speed 103010 Replicate 3 descriptors 10 times 002163 Height assignment method 007004 Pressure 012001 Temperature #<PHONE_NUMBER>31 In direction of 0 degree longitude, distance from the Earth’s centre 028031 In direction of 90 degrees East longitude, distance from the Earth’s centre 010031 In direction of North Pole, distance from Earth’s centre #<PHONE_NUMBER>41 Absolute platform velocity - first component 001042 Absolute platform velocity - second component 001043 Absolute platform velocity - third component #<PHONE_NUMBER>53 Satellite channel centre frequency 002154 Satellite channel band width 020081 Cloud amount in segment 020082 Amount segment cloud free 020012 Cloud type #<PHONE_NUMBER>52 Satellite instrument used in data processing 002166 Radiance type 002167 Radiance computational method 002153 Satellite channel centre frequency 002154 Satellite channel band width 012075 Spectral radiance 012076 Radiance 012063 Brightness temperature
#<PHONE_NUMBER>32 Indicator for digitization 102000 Delayed replication of 2 descriptors 031001 Replication factor 007062 Depth below sea surface 022042 Subsurface sea temperature #<PHONE_NUMBER>31 Method of current measurement 022004 Direction of current 022031 Speed of current #<PHONE_NUMBER>02 Wind instrumentation 011011 Wind direction (10 m) 011012 Wind speed (10 m) 012004 Dry-bulb temperature (2 m) #<PHONE_NUMBER>32 Indicator for digitization 002033 Method of salinity/depth measurement 103000 Delayed replication of 3 descriptors 031001 Replication factor 007062 Depth below sea surface 022043 Subsurface sea temperature 022062 Salinity #<PHONE_NUMBER>31 Method of current measurement 103000 Delayed replication of 3 descriptors 031001 Replication factor 007062 Depth below sea surface 022004 Direction of current 022031 Speed of current #<PHONE_NUMBER>03 Surface wind and temperature 306002 Current 022063 Total water depth #<PHONE_NUMBER>12 Direction of motion of moving observing platform 001014 Platform drift speed (high precision) 306008 Buoy instrumentation 004024 Time period 027003 Alternate latitude 028003 Alternate longitude #<PHONE_NUMBER>34 Drogue type 002035 Cable length 002036 Buoy type #<PHONE_NUMBER>24 Tide station identification, date/time, water temperature, tide station tide data status, time period or displacement, time increment 102006 Replicate 2 descriptors 6 times 022038 Tidal elevation with respect to local chart datum 022039 Meteorological residual tidal elevation #<PHONE_NUMBER>75 Tide station alphanumeric identification 301011 Year, month, day 301012 Hour, minute 022122 Tide station automated meteorological data check 022123 Tide station manual meteorological data check 012001 Air temperature 303002 Pressure, wind direction, wind speed #<PHONE_NUMBER>75 Tide station alphanumeric identification 301011 Year, month, day 301012 Hour, minute 022038 Tidal elevation with respect to local chart datum 022039 Meteorological residual tidal elevation #<PHONE_NUMBER>15 Station or site name 301023 Latitude, longitude 301011 Year, month, day 301012 Hour, minute 022038 Tidal level with respect to local chart datum 022039 Meteorological residual tidal elevation 022120 Tide station automated water level check 022121 Tide station manual water level check #<PHONE_NUMBER>75 Tide station alphanumeric identification 301011 Year, month, day 301012 Hour, minute 022042 Sea/water temperature 022120 Tide station automated water level check 022121 Tide station manual water level check 004025 Time period or displacement 004015 Time increment
#<PHONE_NUMBER>31 Identification, type, date/time, position (high accuracy), height 302011 Basic surface report #<PHONE_NUMBER>32 Identification, type, date/time, position (coarse accuracy), height 302011 Basic surface report #<PHONE_NUMBER>01 Location (high accuracy) and basic report 101000 Delayed replication of 1 descriptor 031001 Replication factor 302005 Cloud layer information #<PHONE_NUMBER>02 Location (coarse accuracy) and basic report 101000 Delayed replication of 1 descriptor 031001 Replication factor 302005 Cloud layer information #<PHONE_NUMBER>01 Location (high accuracy) and basic report 101004 Replicate 1 descriptor 4 times 302005 Cloud layer information (4 layers) #<PHONE_NUMBER>02 Location (coarse accuracy) and basic report 101004 Replicate 1 descriptor 4 times 302005 Cloud layer information (4 layers) #<PHONE_NUMBER>31 Identification, type, date/time, position (high accuracy), height 302012 Basic surface report #<PHONE_NUMBER>32 Identification, type, date/time, position (coarse accuracy), height 302012 Basic surface report #<PHONE_NUMBER>31 Identification, type, date/time, position (high accuracy), height 302013 Basic surface report #<PHONE_NUMBER>63 ICAO location indicator CCCC 002001 Type of station (AUTO) 301011 Year, month, day (YY) 301012 GG, gg 301024 Latitude-longitude (coarse accuracy), height of station 007006 Height above station (= height of an anemometer) 011001 Wind direction ddd 011016 Extreme counterclockwise wind direction of a variable wind dndndn 011017 Extreme clockwise wind direction of a variable wind dxdxdx 011002 Wind speed ff 011041 Maximum wind speed (gusts) fmfm 007006 Height above station (= height of a thermometer) 012001 Temperature T´T´ 012003 Dew-point temperature T´dT´d 010052 Altimeter setting (QNH) PHPHPHPH 020009 General Weather Indicator TAF/METAR #<PHONE_NUMBER>00 Delayed replication of 3 descriptors 031001 Number of replication (up to 3) 008023 First order statistics 005021 Direction of visibility observed Dv 020001 Horizontal visibility VVVV #<PHONE_NUMBER>00 Delayed replication of 6 descriptors 031001 Number of replication (up to 4) 001064 Runway designator DRDR 008014 Qualification for runway visual range 020061 Runway visual range VRVRVRVR 008014 Qualification for runway visual range 020061 Runway visual range VRVRVRVR 020018 Tendency of runway visual range i #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Number of replication (up to 3) 020019 Significant present weather w´w´ #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Number of replication 302005 (NsNsNs, CC, hshshs) 020002 Vertical visibility VVhshshs #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Number of replication (up to 3) 020020 Significant recent weather phenomena REw´w´ #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Number of replication 011070 Runway designator of the runway affected by wind shear (including ALL) WS RWYDRDR #<PHONE_NUMBER>16 Change qualifier of a trend-type forecast or an aerodrome forecast TTTTT 102000 Delayed replication of 2 descriptors 031001 Number of replication (up to 2) 008017 Qualifier of the time when the forecast change is expected (FM, TL, AT) TT 301012 GG, gg 104000 Delayed replication of 4 descriptor 031001 Number of replication (up to 1) 007006 Height above station 011001 Wind direction ddd 011002 Wind speed ff 011041 Maximum wind speed (gusts) fmfm 020009 General Weather Indicator 101000 Delayed replication of 1 descriptor 031001 Number of replication (up to 1) 020001 Horizontal visibility VVVV 307014 w´w´ #<PHONE_NUMBER>11 Main part of data 307014 w´w´ 307016 REw´w´ #<PHONE_NUMBER>11 Main part of data 307012 DvVVVV 307013 DRDRVRVRVRVR 307014 w´w´ 307015 Clouds group(s) 307016 REw´w´ 307017 Wind shear on runway(s) 307018 Trend-type landing forecast 307015 Clouds group(s)
#<PHONE_NUMBER>33 Identification, type, date/time, position (high accuracy) 302011 Basic surface report 022042 Sea-surface temperature #<PHONE_NUMBER>34 Identification, type, date/time, position (coarse accuracy) 302011 Basic surface report 022042 Sea-surface temperature #<PHONE_NUMBER>35 Identification, movement, type, date/time, position (coarse accuracy) 302011 Basic surface report 022042 Sea-surface temperature #<PHONE_NUMBER>36 Identification, movement, type, date/time, position (coarse accuracy) 302011 Basic surface report 022042 Sea-surface temperature #<PHONE_NUMBER>04 Basic ship report 302024 Wind waves and swell waves #<PHONE_NUMBER>04 Pressure 010061 3-hour pressure change 010063 Characteristic of pressure tendency 011001 Wind direction 011002 Wind speed 012004 Dry-bulb temperature at 2 m 013003 Relative humidity 022042 Sea temperature #<PHONE_NUMBER>55 Identification, movement type, date/time, position (high accuracy) 302011 Basic surface report 007062 Depth below sea/water surface 022042 Sea/water temperature
#<PHONE_NUMBER>37 Identification, etc. (land station, high accuracy position) 101000 Delayed replication of 1 descriptor 031001 Replication factor 303011 Winds at heights #<PHONE_NUMBER>38 Identification, etc. (land station, coarse accuracy position) 101000 Delayed replication of 1 descriptor 031001 Replication factor 303011 Winds at heights #<PHONE_NUMBER>37 Identification, etc. (land station, high accuracy position) 101000 Delayed replication of 1 descriptor 031001 Replication factor 303012 Winds at pressure levels #<PHONE_NUMBER>38 Identification, etc. (land station, coarse accuracy position) 101000 Delayed replication of 1 descriptor 031001 Replication factor 303012 Winds at pressure levels #<PHONE_NUMBER>37 Identification, etc. (land station, high accuracy position) 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303013 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>38 Identification, etc. (land station, coarse accuracy position) 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303013 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>37 Identification, etc. (land station, high accuracy position) 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303014 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>38 Identification, etc. (land station, coarse accuracy position) 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303014 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>39 Ship's identification, etc. 101000 Delayed replication of 1 descriptor 031001 Replication factor 303011 Winds at heights #<PHONE_NUMBER>39 Ship's identification, etc. 101000 Delayed replication of 1 descriptor 031001 Replication factor 303012 Winds at pressure levels #<PHONE_NUMBER>39 Ship's identification, etc. 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303013 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>39 Ship's identification, etc. 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303014 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>40 Ship's identification, etc. 101000 Delayed replication of 1 descriptor 031001 Replication factor 303011 Winds at heights #<PHONE_NUMBER>40 Ship's identification, etc. 101000 Delayed replication of 1 descriptor 031001 Replication factor 303012 Winds at pressure levels #<PHONE_NUMBER>40 Ship's identification, etc. 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303013 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>40 Ship's identification, etc. 302004 Significant cloud information 101000 Delayed replication of 1 descriptor 031001 Replication factor 303014 Pressure, geopotential, temperature and wind data #<PHONE_NUMBER>31 Identification, etc. 002003 Type of measuring equipment used 101000 Delayed replication of 1 descriptor 031001 Replication factor 303011 Winds at heights #<PHONE_NUMBER>31 Identification, etc. 002003 Type of measuring equipment used 104000 Delayed replication of 4 descriptors 031001 Replication factor 007003 Geopotential 011003 u-component 011004 v-component 011005 w-component
#<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303032 Cloud 101026 Replicate 1 descriptor 26 times 303025 Satellite channel and brightness temperature #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303032 Cloud 101009 Replicate 1 descriptor 9 times 303023 Layer mean temperature #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303032 Cloud 101006 Replicate 1 descriptor 6 times 303023 Layer mean temperature #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303032 Cloud 101003 Replicate 1 descriptor 3 times 303024 Precipitable water #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303033 Cloud 101000 Delayed replication of 1 descriptor 031001 Delayed descriptor replication factor 303025 Satellite channel and brightness temperature #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303033 Cloud 101000 Delayed replication of 1 descriptor 031001 Delayed descriptor replication factor 303023 Layer mean temperature #<PHONE_NUMBER>42 Identification, method, date/time 303031 Significance data, land/sea, skin temperature 303033 Cloud 101000 Delayed replication of 1 descriptor 031001 Delayed descriptor replication factor 303024 Precipitable water #<PHONE_NUMBER>11 ATOVS field of view variables 101019 Replicate 1 descriptor 19 times 310012 ATOVS channel variables 002150 TOVS/ATOVS/AVHRR instrumentation channel number 025079 Albedo-radiance solar filtered irradiance for ATOVS 025080 Albedo-radiance equivalent filter width for ATOVS 033032 Channel quality flags for ATOVS 014045 Channel radiance #<PHONE_NUMBER>11 ATOVS field of view variables 101015 Replicate 1 descriptor 15 times 310012 ATOVS channel variables #<PHONE_NUMBER>11 ATOVS field of view variables 101005 Replicate 1 descriptor 5 times 310012 ATOVS channel variables #<PHONE_NUMBER>70 TOVS/ATOVS product qualifier 001033 Identification of originating/generating centre 001034 Identification of originating/generating centre 008070 TOVS/ATOVS product qualifier 001033 Identification of originating/generating centre 001034 Identification of originating/generating centre 001007 Satellite identification 002048 Satellite sensor indicator 005040 Orbit number 025075 Satellite antenna corrections version number 201133 Change width 005041 Scan line number 201000 Change width 005043 Field of view number 025070 Major frame count 033030 Scan line status flags for ATOVS 033031 Scan line quality flags for ATOVS 004001 Year 004002 Month 004003 Day 004004 Hour 004005 Minute 202131 Change scale 201138 Change width 004006 Second 201000 Change width 202000 Change scale 005001 Latitude 006001 Longitude 202126 Change scale 007001 Height of station 202000 Change scale 007024 Satellite zenith angle 005021 Satellite azimuth 007025 Solar zenith angle 005022 Solar azimuth 033033 Field of view quality flags for ATOVS 002151 Radiometer identifier 012064 Instrument temperature 002151 Radiometer identifier 012064 Instrument temperature 002151 Radiometer identifier 012064 Instrument temperature 002151 Radiometer identifier 012064 Instrument temperature #<PHONE_NUMBER>50 TOVS/ATOVS/AVHRR instrumentation channel number 025076 Log-10 of (temperature-radiance central wavenumber) for ATOVS 025077 Bandwidth correction coefficient 1 for ATOVS 025078 Bandwidth correction coefficient 2 for ATOVS 033032 Channel quality flags for ATOVS 201132 Change width 202129 Change scale 012063 Brightness temperature 202000 Change scale 201000 Change width #<PHONE_NUMBER>07 Satellite ID 005040 Orbit number 004001 Year 004002 Month 004003 Day 004004 Hour 004005 Minute 004006 Second 005001 Latitude 006001 Longitude 007025 Solar zenith angle 005043 Field of view number 025085 Fraction of clear pixels in HIRS field of view 201131 Change width 202129 Change scale 002150 TOVS/ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 014027 Albedo 008072 Pixel(s) type 014027 Albedo 002150 TOVS/ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 014027 Albedo 008072 Pixel(s) type 014027 Albedo 002150 ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 014027 Albedo 008072 Pixel(s) type 014027 Albedo 202000 Change scale 201000 Change width 201132 Change width 202129 Change scale 002150 ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 012063 Brightness temperature 008072 Pixel(s) type 012063 Brightness temperature 002150 ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 012063 Brightness temperature 008072 Pixel(s) type 012063 Brightness temperature 008023 First order statistics 008072 Pixel(s) type 012063 Brightness temperature 008072 Pixel(s) type 012063 Brightness temperature 002150 ATOVS/AVHRR instrumentation channel number 008023 First order statistics 008072 Pixel(s) type 012063 Brightness temperature 008072 Pixel(s) type 012063 Brightness temperature 202000 Change scale 201000 Change width #<PHONE_NUMBER>72 Satellite identification, date, time, latitude, longitude 303041 Wind sequence 304011 GOES-I/M information #<PHONE_NUMBER>72 Satellite identification 007024 Satellite zenith angle 010002 Height 303041 Wind sequence 101003 Replicate next descriptor 3 times 304032 Cloud fraction 002152 Satellite instrument used in data processing 002024 Integrated mean humidity computational method 007004 Pressure 007004 Pressure 013003 Relative humidity 101003 Replicate next descriptor 3 times 304033 Clear sky radiance #<PHONE_NUMBER>72 Satellite identification 007024 Satellite zenith angle 010002 Height 303041 Wind sequence 101012 Replicate next descriptor 12 times 304032 Cloud fraction 002152 Satellite instrument used in data processing 002024 Integrated mean humidity computational method 007004 Pressure 007004 Pressure 013003 Relative humidity 101012 Replicate next descriptor 12 times 304033 Clear sky radiance
#<PHONE_NUMBER>51 ASDAR aircraft identifier, navigational system, date/time, position, phase of aircraft flight 007002 Altitude 012001 Temperature 011001 Wind direction 011002 Wind speed 011031 Degree of turbulence 011032 Height of base of turbulence 011033 Height of top of turbulence 020041 Airframe icing #<PHONE_NUMBER>65 ACARS identification 301066 ACARS location 311003 ACARS standard reported variables 311004 ACARS supplementary reported variables #<PHONE_NUMBER>70 Indicated aircraft altitude 011001 Wind direction 011002 Wind speed 012001 Temperature/dry-bulb temperature 013002 Mixing ratio #<PHONE_NUMBER>00 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 011034 Vertical gust velocity 101000 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 011035 Vertical gust acceleration 101000 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 011075 Mean turbulence intensity (eddy dissipation rate) 101000 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 011076 Peak turbulence intensity (eddy dissipation rate) 101000 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 033025 ACARS interpolated values 101000 Delayed replication of one descriptor 031000 Short delayed descriptor replication factor 033026 Mixing ratio quality
#<PHONE_NUMBER>43 Satellite identifier, instrumentation, location, date/time 304001 Cloud top pressure, temperature, wind #<PHONE_NUMBER>43 Satellite identifier, instrumentation, location, date/time 304002 Cloud top pressure, wind #<PHONE_NUMBER>42 Satellite identifier, instrumentation, location, date/time 304003 Surface temperature #<PHONE_NUMBER>42 Satellite identifier, instrumentation, location, date/time 304004 Cloud cover #<PHONE_NUMBER>42 Satellite identifier, instrumentation, location, date/time 020014 Height of top of cloud #<PHONE_NUMBER>44 Satellite identifier, instrumentation, location, date/time 304005 Layer mean relative humidity #<PHONE_NUMBER>42 Satellite identifier, instrumentation, location, date/time 304006 Radiation #<PHONE_NUMBER>07 Satellite identifier 005040 Orbit number 002021 Satellite instrumentation 005041 Scan line number 004001 Year 004043 Day of year #<PHONE_NUMBER>31 Change scale 201149 Change width 004006 Second 201000 Change width 202126 Change scale 010002 Height 202000 Change scale 005043 Field of view number 005053 Field of view number increment #<PHONE_NUMBER>29 Change scale 201132 Change width 101019 Replicate 1 descriptor 19 times 012063 Brightness temperature 201000 Change width 202000 Change scale #<PHONE_NUMBER>42 Channel number 202129 Change scale 201135 Change width 012063 Brightness temperature 201000 Change width 202000 Change scale #<PHONE_NUMBER>10 Orbital information, Part I 312011 Orbital information, Part II 105056 Replicate 5 descriptors 56 times 301023 Latitude and longitude (coarse accuracy) 005042 Channel number 005052 Channel number increment 312012 HIRS brightness temperatures - channels 1-19 312013 HIRS brightness temperature - channel 20 #<PHONE_NUMBER>11 Replicate 9 descriptors 11 times 301023 Latitude and longitude (coarse accuracy) 005042 Channel number 005052 Channel number increment 202129 Change scale 201132 Change width 101004 Replicate 1 descriptor 4 times 012063 Brightness temperature 202000 Change scale 201000 Change width #<PHONE_NUMBER>10 Orbital information, Part I 312011 Orbital information, Part II 312015 MSU brightness temperatures - channels 1-4 #<PHONE_NUMBER>08 Replicate 9 descriptors 8 times 301023 Latitude and longitude (coarse accuracy) 005042 Channel number 005052 Channel number increment 202129 Change scale 201132 Change width 101003 Replicate 1 descriptor 3 times 012063 Brightness temperature 202000 Change scale 201000 Change width #<PHONE_NUMBER>10 Orbital information, Part I 312011 Orbital information, Part II 312017 SSU brightness temperatures - channels 1-3 #<PHONE_NUMBER>47 Product header 301048 Radar parameters 015015 Maximum spectrum composition before normalisation 029002 Coordinate grid type 021076 Representation of intensities 106012 Repeat next 6 descriptors 12 times 201129 Change width to 14 bits 006030 Wave number (spectral) 201000 Change width to Table B 102012 Repeat next 2 descriptors 12 times 005030 Direction (spectral) 021075 Image spectrum intensity 021066 Wave scatterometer product confidence data #<PHONE_NUMBER>47 Product header 301048 Radar parameters 015015 Maximum spectrum composition before normalization 029002 Coordinate grid type 021076 Representation of intensities 104012 Repeat next 4 descriptors 12 times 006030 Wave number (spectral) 102012 Repeat next 2 descriptors 12 times 005030 Direction (spectral) 021075 Spectral intensity 021066 Wave scatterometer product confidence data #<PHONE_NUMBER>47 Product header 101003 Repeat 1 descriptor 3 times 301049 Radar beam data 011012 Wind speed at 10 m 011011 Wind direction at 10 m 021067 Wind product confidence data #<PHONE_NUMBER>47 Product header 008022 Number in average 011012 Wind speed 011050 Standard deviation of horizontal wind speed 022070 Significant wave height 022026 Standard deviation of significant wave height 312041 Altitude 010050 Standard deviation of altitude 021068 Radar altimeter product confidence data 021071 Peakiness 021072 Altimeter calibration status 021073 Altimeter instrument mode 312042 Altitude corrections 021062 Backscatter 015011 Log 10 of integrated electron density #<PHONE_NUMBER>47 Product header 103003 Repeat 3 descriptors 3 times 008022 Number in average 012061 Skin temperature 022050 Standard deviation of sea surface temperature 021069 SST product confidence data 021085 ATSR sea surface temperature across-track band number #<PHONE_NUMBER>20 (Wave scatterometer product) 008060 Sample scanning mode significance - range 008022 Number in sample 008060 Sample scanning mode signification - horizontal 008022 Number in sample 025014 Azimuth clutter cut-off 022101 Total energy (wavelength > 731 m) 022097 Mean wavelength of image spectrum 022098 Wavelength spread (wavelength > 731 m) 022099 Mean direction (wavelength > 731 m) 022100 Direction spread (wavelength > 731 m) #<PHONE_NUMBER>19 Wave scatterometer product with width change for wave number (spectral) 008060 Sample scanning mode significance - range 008022 Number in sample 008060 Sample scanning mode significance - horizontal 008022 Number in sample 025014 Azimuth clutter cut-off 022101 Total energy (wavelength > 731 m) 022097 Mean wavelength of image spectrum 022098 Wavelength spread (wavelength > 731 m) 022099 Mean direction (wavelength > 731 m) 022100 Direction spread (wavelength > 731 m) #<PHONE_NUMBER>46 301011 Data 301013 Time 301023 Location<PHONE_NUMBER>04 Replicate 1 descriptor 4 times<PHONE_NUMBER>10 Number of inner-beam sigma-0 (forward of satellite) 301023 Location<PHONE_NUMBER>11 Number of outer-beam sigma-0 (forward of satellite) 301023 Location<PHONE_NUMBER>12 Number of inner-beam sigma-0 (aft of satellite) 301023 Location<PHONE_NUMBER>13 Number of outer-beam sigma-0 (aft of satellite) 301023 Location 321027 #<PHONE_NUMBER>30 Change data width 202129 Change scale 011012 Wind speed at 10 m 202000 Change scale back to Table B 201000 Change data width to Table B 011052 Formal uncertainty in wind speed 201135 Change data width 202130 Change scale 011011 Wind direction at 10 m 202000 Change scale back to Table B 201000 Change data width to Table B 011053 Formal uncertainty in wind speed 021104 Likelihood computed for solution #<PHONE_NUMBER>34 Along-track row number 006034 Cross-track cell number 021109 SEAWINDS wind vector cell quality 011081 Model wind direction at 10 m 011082 Model wind speed at 10 m 021101 Number of vector ambiguities 021102 Index of selected wind vector 021103 Total number of sigma-0 measurements #<PHONE_NUMBER>41 Change width to 28 bits 202130 Change scale to 2 007001 Altitude 201000 Change width back to Table B 202000 Change scale back to Table B #<PHONE_NUMBER>77 Altitude correction, ionosphere 021078 Altitude correction, dry troposphere 021079 Altitude correction, wet troposphere 021080 Altitude correction, calibration constant 021081 Open loop height-time loop calibration correction 021082 Open loop automatic gain control calibration correction
#<PHONE_NUMBER>01 Horizontal reflectivity 101000 Delayed replication of 1 descriptor 031001 Replication factor 021001 Horizontal reflectivity #<PHONE_NUMBER>36 Radar rainfall intensity 101000 Delayed replication of 1 descriptor 031001 Replication factor 021036 Radar rainfall intensity #<PHONE_NUMBER>02 First longitude location minus one increment 006012 Longitude increment 101000 Delayed replication of 1 descriptor 031002 Extended replication factor 030001 Pixel value (4 bits) #<PHONE_NUMBER>02 First latitude location minus one increment 005012 Latitude increment (signed value so cannot cross pole) 101000 Delayed replication of 1 descriptor 031002 Extended replication factor 313031 Non run-length encoded row #<PHONE_NUMBER>02 First longitude location minus one increment 110000 Delayed replication of 10 descriptors 031001 Replication factor 104000 Delayed replication of 4 descriptors 031001 Replication factor 006012 Longitude increment 101000 Delayed replication of 1 descriptor 031012 Repetition factor 030001 Pixel value (4 bits) 006012 Longitude increment 101000 Delayed replication of 1 descriptor 031001 Replication factor 030001 Pixel value (4 bits) #<PHONE_NUMBER>02 First latitude location minus one increment 005012 Latitude increment (signed value so cannot cross pole) 101000 Delayed replication of 1 descriptor 031002 Extended replication factor 313041 Run-length encoded row #<PHONE_NUMBER>02 First longitude location minus one increment 005002 First latitude location minus one increment 005012 Latitude increment 112000 Delayed replication of 12 descriptors 031001 Replication factor 110000 Delayed replication of 10 descriptors 031001 Replication factor 104000 Delayed replication of 4 descriptors 031001 Replication factor 006012 Longitude increment 101000 Delayed replication of 1 descriptor 031011 Repetition factor 030001 Pixel value (4 bits) 101000 Delayed replication of 1 descriptor 031001 Replication factor 030001 Pixel value (4 bits)
#<PHONE_NUMBER>11 Ship's call sign 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) 306001 Depth, temperature #<PHONE_NUMBER>11 Ship's call sign 301011 Date 301012 Time 301023 Latitude and longitude (coarse accuracy) 306004 Depth, temperature, salinity
#<PHONE_NUMBER>11 Year, month, day 004004 Hour 301023 Latitude and longitude (coarse accuracy) 001021 Synoptic feature identifier 002041 Method for estimating reports related to synoptic features 019001 Type of synoptic feature 010051 Pressure reduced to mean sea level 019002 Effective radius of feature 019003 Wind speed threshold (15 m s-1 typically) 019004 Effective radius with respect to wind speeds above threshold #<PHONE_NUMBER>21 Data time (analysis) 004001 Year 004002 Month 004003 Day 004004 Hour 004005 Minute 001033 Originating/generating centre 008021 Validity time (fcst) 004001 Year 004002 Month 004003 Day 004004 Hour 004005 Minute 007002 Flight level (altitude) (base of chart layer) 007002 Flight level (altitude) (top of chart layer) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature (jet stream value) 008007 Dimensional significance (value for line) 104000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 010002 Flight level (altitude) 011002 Wind speed 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature (value for turbulence) 008007 Dimensional significance (value for area) 007002 Flight level (altitude) (base of layer) 007002 Flight level (altitude) (top of layer) 102000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 011031 (1) Degree of turbulence 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008005 Meteorological attribute significance (storm centre) 008007 Dimensional significance (value for point) 005002 Latitude (coarse) 006002 Longitude (coarse) 001026 WMO storm name (use “unknown” for a sandstorm) 019001 Synoptic features (value for type of storm) 008007 Dimensional significance (cancel) 008005 Meteorological attribute significance (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature (value for cloud) 008007 Dimensional significance (value for area) 007002 Flight level (altitude) (base of layer) 007002 Flight level (altitude) (top of layer) 102000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 020011 (2) Cloud amount 020012 Cloud type 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 (3) Meteorological feature (value for type of front) 008007 Dimensional significance (value for line) 104000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 019005 Direction of feature 019006 Speed of feature 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008001 Vertical significance (bit 3 set for tropopause) 008007 Dimensional significance (value for point) 008023 (4) Statistic (type of tropopause value) 103000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 010002 Height/altitude 008023 Statistic (cancel) 008007 Dimensional significance (cancel) 008001 Vertical significance (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature (value for airframe icing) 008007 Dimensional significance (value for area) 007002 Flight level (altitude) (base of layer) 007002 Flight level (altitude) (top of layer) 102000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 020041 Airframe icing (type of airframe icing) 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature 008007 Dimensional significance (value for point) 001022 Name of feature 005002 Latitude (coarse) 006002 Longitude (coarse) 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object) #<PHONE_NUMBER>00 Delayed replication 031001 Replication 008011 Meteorological feature (value for special clouds) 001022 Name of feature (volcano name) 008007 Dimensional significance (value for point) 102000 Delayed replication 031001 Replication 005002 Latitude (coarse) 006002 Longitude (coarse) 008021 Time significance (eruption starting time) 004001 Year 004002 Month 004003 Day 004004 Hour 004005 Minute 020090 Special clouds (clouds from volcanic eruptions) 008021 Time significance (cancel) 008007 Dimensional significance (cancel) 008011 Meteorological feature (cancel/end of object)
#<PHONE_NUMBER>25 Latitude and longitude (coarse accuracy), day and time 024011 Dose #<PHONE_NUMBER>26 Latitude and longitude (high accuracy), time periods in days, hours and minutes 024005 Isotope mass 024004 Element name 024021 Air concentration #<PHONE_NUMBER>25 Latitude and longitude (coarse accuracy), day and time 004023 Time period or displacement 013011 Total precipitation/total water equivalent 024005 Isotope mass 024004 Element name 024022 Concentration in precipitation
#<PHONE_NUMBER>01 Type of antenna 002114 Antenna effective surface area 002105 Maximum antenna gain 002106 3-dB beamwidth 002107 Sidelobe suppression 002121 Mean frequency #<PHONE_NUMBER>51 Signal power above 1 mW 021014 Doppler mean velocity (radial) 021017 Doppler velocity spectral width 021030 Signal to noise ratio #<PHONE_NUMBER>31 Identification, type, date/time, position (high accuracy), height 002003 Type of measuring equipment used 101000 Delayed replication of 1 descriptor 031001 Replication factor 321003 Wind profiler - moment data #<PHONE_NUMBER>04 Echo processing 002121 Mean frequency 002122 Frequency agility range 002123 Peak power 002124 Average power 002125 Pulse repetition frequency 002126 Pulse width 002127 Receiver intermediate frequency 002128 Intermediate frequency bandwidth 002129 Minimum detectable signal 002130 Dynamic range 002131 Sensitivity time control #<PHONE_NUMBER>01 Range-gate length 025002 Number of gates averaged 025003 Number of integrated pulses 025005 Echo integration #<PHONE_NUMBER>09 Calibration method 025010 Clutter treatment 025011 Ground occultation correction 025012 Range attenuation correction 025013 Bright-band correction 025015 Radome attenuation correction 025016 Clear-air attenuation correction 025017 Precipitation attenuation correction #<PHONE_NUMBER>06 Z to R conversion 025007 Z to R conversion factor 025008 Z to R conversion exponent #<PHONE_NUMBER>18 A to Z law for attenuation factor 025019 A to Z law for attenuation exponent #<PHONE_NUMBER>01 Type of antenna 007002 Altitude of the tower base 002102 Antenna height above tower base 002103 Radome 002104 Antenna polarisation 002105 Maximum antenna gain 002106 3-dB beamwidth 002107 Sidelobe suppression 002108 Crosspol discrimination (on axis) 002109 Antenna speed (azimuth) 002110 Antenna speed (elevation) 002132 Azimuth pointing accuracy 002133 Elevation pointing accuracy #<PHONE_NUMBER>31 Picture type 030032 Combination with other data 029002 Coordinate grid type #<PHONE_NUMBER>00 Delayed replication of 1 descriptor 031001 Replication factor 002135 Antenna elevation #<PHONE_NUMBER>18 Attenuation correction on sigma-0 202129 Change scale 201132 Change data width 002112 Radar look angle 201000 Change data width to Table B 201131 Change data width 002111 Radar incidence angle 201000 Change data width to Table B 202000 Change scale back to Table B 002104 Antenna polarization 021105 Normalized radar cross-section 021106 Kp variance coefficient (alpha) 021107 Kp variance coefficient (beta) 021114 Kp variance coefficient (gamma) 021115 SEAWINDS sigma-0 quality 021116 SEAWINDS sigma-0 mode 008018 SEAWINDS land/ice surface type 021117 Sigma-0 variance quality control #


Great Horned Owls in Athens
It's entirely possible to see and hear these magnificent owls in the wilds of Athens, GA. You can learn more than the casual observer by clicking the buttons below...
Taxonomy
| Order |
Strigiformes |
| Family |
Strigidae |
| Genus |
Bubo |
| Species |
virginiana |
Diet
- In the wild, great horned owls eat a variety of mammals, like mice, rats, rabbits, squirrels, skunks, raccoons, and other small to medium sized prey. Birds are also taken and occasionally snakes or frogs.
- At Bear Hollow, the great horned owls eat a rotation of chicks, small rats and mice.
Range and Habitat
- An incredibly wide ranging owl. They can be found across the entire North American continent, just south of the arctic tree-line, down through Central America and in a broad swatch across central South America.
- They occur statewide in Georgia.
- Habitats include both deciduous and coniferous forests, swampland, mountains, grasslands and urban areas.
Physical Features
- Very large, stocky owl. Feathers are brownish or grayish, with light and dark stippling and spotting. Often have orangeish highlights.
- Facial disks are most often brownish orange and eyes are big and bright yellow. Two large father tufts on top of the head.
- Body length is 18 to 27 inches. Wingspan is between 48 and 60 inches from wingtip to wingtip. Great horned owls usually weigh around 3 lbs. as adults.
Life Span
- In the wild, great horned owls can live up to thirteen years of age.
- In captivity, it is not unheard of for a great horned owl to live into their 30’s. The record is over 38 years.
Reproduction
- Pairs begin courtship in late fall. Breeding season is in December, and the two eggs are usually laid in January.
- Nests are either taken over from squirrels, crows or small hawks, or they may create their own in a large hollow tree, cave or protected rooftop.
- The eggs are incubated for about 33 days, and the young then leave the nest after about 21 days. Young owlets stay with their parents until he start of the new breeding season.
Social Behavior
- Great horned owls that have formed mated pairs are permanent residents within their territories.
- However, they do not often stay together except during the breeding season. They will share a territory but remain apart in their separate areas.
Conservation
- A species of little conservation concern. Populations seem to be common and secure.
- However, they are completely protected by both state and federal laws.
Interpretive Information
- Owls’ ears are not symmetrical. The right ear opening is usually set higher than the left, and it tends to open at a different angle too. Often it may be a different size as well.
- The typical ‘hoot owl’, great horned owls have a five syllable call: ‘hoo-hoo hoooo hoo-hoo’ rapid at first, then slower and lower for the last two syllables. It is often likened to “Who’s awake? Me, too.” Males have a deeper voice than the females.
- Great horned owls are considered to be the nocturnal counterpart of the red tailed hawks.
Bear Hollow’s Great Horned Owls
- Male: Eeyore
- Hatched in the wild at an unknown time. Rehabilitated at Carolina Raptor Center and arrived at Bear Hollow in 2000.
- Female: Winnie the Hoo
- Hatched in the wild at an unknown time. Hit by car in Indiana and rode all the way to Athens on the grill of the car. Taken to the UGA vet hospital and rehabilitated there. Arrived at Bear Hollow in September 2008.
Credits and References
- Photo © George Jameson
- General research by Sherrie Hines


We are advising our client with how to best use their land bank worth over INR 100 Crores, in and around Dehradun, in form of 500+ Bighas vis a vis a correct concept and connections with progressive infrastructure companies to facilitate JV and partnerships.
Our client is also open to the idea of the complete sale of the land.


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My name is Bohong, I am a recent undergraduate student with B.S. in computer science and substantial hands-on training. Bilingual, fluent in Chinese and English. Aspires to bring technical training and expertise to challenging internship or full-time position as a Front-End Web Developer or Web Developer.
Becoming a programmer was not my first choice. I love high-tech things since I was young, so the first time I know about coding was when I found out the "magic" that made them do things differently was some kind of "alien language" which really interests me. That's why I chose the "alien language" which is computer science as my major and turned out I was pretty into it. Web development really caught my eyes after coding for a few years. Since we didn't have any related courses in college, I decided to learn on my own. And here's what I have made. The more I know about it, the more I like it, I could spend all day long writing and fixing a website because I enjoy the beautiful page that shows up on the screen after I finish all the code. That's the moment that I'm so proud of myself.
ResumeIt is the first portfolio that wirte when I just learned HTML, CSS and a little javaScript.
Want to know more about me?
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Bonus Mode!
Bonus Mode!
Difference: {{ formatDp(diffMean) }}
Is there really a difference?
We have a difference of {{ formatDp(diffMean) }} between the averages of the two catches. It could be that the rivers are the same (a.k.a the null hypothesis) and we just so happened to get this difference of {{ formatDp(diffMean) }}.
What bootstrapping tries to find out:
What is the probability that we just so happened to get this difference of {{ formatDp(diffMean) }}, assuming the rivers are the same?
Or in more technical terms:
What is the likelihood of the observed data, given that the null hypothesis is true (a.k.a. the p-value)?
If this probability is high, then there is a good chance that the rivers are the same. If this probability is low, then the two rivers are probably significantly different.
Difference: {{ formatDp(diffMeanSample) }}
Rinse and Repeat
We can run this many times and record the difference in sample means for each experiment. Then, we can estimate the probability that the difference of {{ formatDp(diffMean) }} (or more) arose out of pure chance!
If the chance of getting the difference from this combined sampling is high, then the difference is probably not significant. Typically, we define high as more than 5%.
Summary
And that, in short, is bootstrapping! We want to test if a difference between two sample distributions could happen purely by chance. So...
As thanks for checking this out, here's a bonus version of this explorable that allows you to change the input distributions! Just click the button below and head back up to the first chart.


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Librarian Larry

Would you please keep it down?  Sausage ground round andouille ball tip.


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library(tidyverse)
library(cowplot)
library(broom)
library(modelr)
library(viridis)
library(lubridate)
library(hms)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")
source("../../tools/echem_processing_tools.R")
source("../../tools/plotting_tools.R")
theme_set(theme_1())
First let’s import the SWV data from all the runs. Now I’m using the “recursive” option in dir() to look through the whole directory strucutre of the raw data files. This allows me to preserve the file structure, which is nice for humans, but still find all the SWV txt files. This works well because the individual files are still named uniquely.
#divide swv into rep and subrep and then subtract 1 from rep to match with GC
SWV_file_paths <- dir(path='../Data', pattern = "[SWV]+.+[txt]$",recursive = T,full.names = T)
SWV_filenames <- basename(SWV_file_paths)
swv_data_cols <- c('E','i1','i2')
filename_cols = c('PHZadded','treatment','reactor','run','echem','rep')
swv_skip_rows=18
swv_data <- echem_import_to_df(filenames = SWV_filenames,
file_paths = SWV_file_paths,
data_cols = swv_data_cols,
skip_rows = swv_skip_rows,
filename_cols = filename_cols,
rep = T) %>%
mutate(rep=rep-1)
Ok, let’s see what the electrode 1 transfer data looks like: (remember there was 1 soak scan taken before each transfer)
ggplot(swv_data %>% filter(electrode=='i1' & reactor=='transfer'), aes(x=E, y=current,color=rep,group=factor(rep)))+geom_path()+
facet_grid(treatment~run,scales='free')+
scale_color_viridis()
Ok, this is the data I expected.
* First, the panel on the far right incorrectly labeled “SWV” is actually the background scan with 0uM PYO, but I’m not going to worry about that now. * Next, we can see that there’s a big difference between the first 3 psoralen runs and the last 2 (4 & 5). For some reason SWV fast produced signals with very high background, starting at the beginning of the experiment. It might still be possible to background subtract them and make use of them, but they look bad…they don’t change very much over the time course. * After the first 3 attempts, I switched to a modified SWV slow for the final 2 psoralen runs, and the high background signal went away. * The control runs look beautiful! The first 3 had about 15 datapoints to measure \(D_{ap}\), and the last 2 had 60 sequential SWVs to measure \(D_m\).
Now let’s look at the same transfer data for electrode 2:
ggplot(swv_data %>% filter(electrode=='i2' & reactor=='transfer'), aes(x=E, y=current,color=rep,group=factor(rep)))+geom_path()+
facet_grid(treatment~run,scales='free')+
scale_color_viridis()
This is also the data I expected, although I admit it is not ideal: * Runs 4 and 5 from the control and psoralen look acceptable, there are clear peaks that diminish. * Unfortunately for runs 1-3 the peaks do not decay, but instead shift and stay relatively constant. Previously, I interpreted this to mean that there is some buildup/reaction that occurs at the collector electrode during GC. Performing the SWV scan immediately after the reductive GC sweep reveals this unexpected product. It is unclear if this is PYO that has built up and for some reason has a shifted peak, or some other side reaction. I do not know how to deal with this, other than hope that it does not affect the reasonable signal measured at electrode 1…
Here’s a closer look at the psoralen SWVs from runs 4 and 5 (electrode 1):
ggplot(swv_data %>% filter(electrode=='i1' & reactor=='transfer' & treatment=='psoralen' & run %in% c(4,5)), aes(x=E, y=current,color=rep,group=factor(rep)))+geom_path()+
facet_wrap(treatment~run,scales='free')+
scale_color_viridis()
They look pretty clean and diminish as expected, so I’ll proceed with these for now.
Now let’s import the GC data for the \(D_{ap}\) estimates.
GC_file_paths <- dir(path='../Data', pattern = "[GC]+.+[txt]$",recursive = T,full.names = T)
GC_filenames <- basename(GC_file_paths)
gc_data_cols <- c('E','i1','i2','t')
filename_cols = c('PHZadded','treatment','reactor','run','echem','rep')
gc_skip_rows=21
gc_data <- echem_import_to_df(filenames = GC_filenames,
file_paths = GC_file_paths,
data_cols = gc_data_cols,
skip_rows = gc_skip_rows,
filename_cols = filename_cols,
rep = T)
Let’s look at all of the GCs taken during transfer:
ggplot(gc_data %>% filter(reactor=='transfer'), aes(x=E, y=current,color=rep,group=electrode))+
geom_point(size=0.5)+
facet_grid(treatment~run,scales='free')+
scale_x_reverse() +
scale_color_viridis(discrete = F)
Ok, this dataset looks pretty much as expected. * The psoralen generators (top i1 lines) look a little funky, as we have seen in the past… * The first rep from each run goes down around -0.4V instead of leveling off, as we expect during the initial period of diminishing PYO concentrations. However, later on (in the control particularly) the GCs seem very symmetrical and do level off (ideal behavior).
Let’s zoom in on the collector data. I’m now plotting the collector data as positive:
ggplot(gc_data %>% filter(reactor=='transfer' & electrode=='i2'), aes(x=E, y=-current,color=rep,group=factor(rep))) +
geom_path()+
facet_grid(treatment~run,scales='free')+
scale_color_viridis()+
scale_x_reverse()
This is the dataset I will quantify, and overall it looks pretty good! In general the signals decay smoothly as the reps go on.
First, let’s quantify the control SWVs, since they are all of similar shape and magnitude (and the psoralen data is not).
swv_control_data <- swv_data %>%
filter(treatment=='control' & PHZaddedInt==75)
unique_id_cols = c('PHZadded','treatment','reactor','run','echem','rep','minutes','PHZaddedInt','electrode')
swv_control_signals <- echem_signal(df = swv_control_data,
unique_id_cols = unique_id_cols,
max_interval = c(0.0,-0.4),
min_interval = c(0.0,-0.4))
First, let’s look at the electrode 1 data, which will be used for further analysis.
ggplot(swv_control_data %>% filter(electrode=='i1'),
aes(x = E, y = current, color = rep, shape=reactor, group=factor(rep))) +
geom_path()+
geom_point(data = swv_control_signals %>% filter(electrode=='i1'),
aes(x = E_from_maxs, y = current_from_maxs), color = 'red') +
geom_point(data = swv_control_signals %>% filter(electrode=='i1'),
aes(x = E_from_mins, y = current_from_mins), color = 'blue') +
facet_wrap(~run,scales='free')+
scale_x_reverse()+
scale_color_viridis()
The quantification looks pretty clean overall: * There are 2 datapoints in run 5 that appear to have max points not in the main redox peak, but let’s ignore those for now. * Interestingly, notice the differences between the soak scan and the first transfer scan (top circle/triangle). In all cases, the scans are pretty close in magnitude. For runs 1 and 4 the transfer peaks are actually slightly higher than the soak…
Now let’s examine the electrode 2 data, even though I don’t intend to use it right now.
ggplot(swv_control_data %>% filter(electrode=='i2'),
aes(x = E, y = current, color = rep, shape=reactor, group=factor(rep))) +
geom_path()+
geom_point(data = swv_control_signals %>% filter(electrode=='i2'),
aes(x = E_from_maxs, y = current_from_maxs), color = 'red') +
geom_point(data = swv_control_signals %>% filter(electrode=='i2'),
aes(x = E_from_mins, y = current_from_mins), color = 'blue') +
facet_wrap(~run,scales='free')+
scale_x_reverse()+
scale_color_viridis()
The quantification looks fine, and we see the same general patterns with the soak/transfer.
Let’s see how the signals (max - min points) actually look across the reps.
ggplot(swv_control_signals, aes(x=rep, y=signal,shape=reactor,color=factor(run)))+geom_line()+geom_point()+
facet_wrap(~electrode)+
scale_color_viridis(discrete = T)
These signals look good and show the same patterns that we would expect from the raw data. You can see the the i2 following the GCs from runs 1-3 look very weird.
swv_psoralen_data <- swv_data %>%
filter(treatment=='psoralen' & PHZaddedInt==75) %>%
filter(run %in% c(4,5))
unique_id_cols = c('PHZadded','treatment','reactor','run','echem','rep','minutes','PHZaddedInt','electrode')
swv_psoralen_signals <- echem_signal(df = swv_psoralen_data,
unique_id_cols = unique_id_cols,
max_interval = c(-0.11,-0.4),
min_interval = c(-0.11,-0.4))
ggplot(swv_psoralen_data %>% filter(electrode=='i1'),
aes(x = E, y = current, color = rep, shape=reactor, group=factor(rep))) +
geom_path()+
geom_point(data = swv_psoralen_signals %>% filter(electrode=='i1'),
aes(x = E_from_maxs, y = current_from_maxs), color = 'red') +
geom_point(data = swv_psoralen_signals %>% filter(electrode=='i1'),
aes(x = E_from_mins, y = current_from_mins), color = 'blue') +
facet_wrap(reactor~run,scales='free')+
scale_x_reverse()+
scale_color_viridis()
gc_data_i2 <- gc_data %>%
filter(electrode=='i2' & PHZaddedInt==75) %>%
mutate(current=-current)
unique_id_cols = c('PHZadded','treatment','reactor','run','echem','rep','minutes','PHZaddedInt','electrode')
gc_signals <- echem_signal(df = gc_data_i2,
unique_id_cols = unique_id_cols,
max_interval = c(-0.399,-0.399),
min_interval = c(0.0,-0.4))
Let’s look at all of the transfer gc data:
ggplot(gc_data_i2 %>% filter(reactor=='transfer'),
aes(x = E, y = current, color = rep, shape=reactor, group=factor(rep))) +
geom_path()+
geom_point(data = gc_signals %>% filter(reactor=='transfer'),
aes(x = E_from_maxs, y = current_from_maxs), color = 'red') +
geom_point(data = gc_signals %>% filter(reactor=='transfer'),
aes(x = E_from_mins, y = current_from_mins), color = 'blue') +
facet_grid(treatment~run,scales='free')+
scale_x_reverse()+
scale_color_viridis()
This quantification looks reasonable. All the maxs are at the endpoints of the run and the mins are in the same regions of the plots.
Let’s look at the soak GCs just to see:
ggplot(gc_data_i2 %>% filter(reactor=='soak' & rep==1),
aes(x = E, y = current, color = factor(run), shape=reactor, linetype=treatment)) +
geom_path()+
geom_point(data = gc_signals %>% filter(reactor=='soak'),
aes(x = E_from_maxs, y = current_from_maxs), color = 'red') +
geom_point(data = gc_signals %>% filter(reactor=='soak'),
aes(x = E_from_mins, y = current_from_mins), color = 'blue') +
scale_x_reverse()+
scale_color_viridis(discrete = T)
Interesting. The psoralen curves are consistently higher than the control, which may be related to the fact that there was more biomass on the control electrode, which may have effectively blocked the solution PYO more than the psoralen biofilm. Also note that these GC magnitudes are almost an order of magnitude higher than the highest transfer GCs. This is different than the SWVs and I’m not sure why. It could be because the first scan taken was an SWV, but it could also be that GC should be more sensitive to changes in \(D_{ap}\) than SWV…
Let’s also look at the GC signals vs. rep:
ggplot(gc_signals, aes(x = rep, y = signal,shape=reactor, color=factor(run)))+
geom_line()+
geom_point()+
facet_wrap(reactor~treatment,scales='free')+
scale_color_viridis(discrete = T)
Control Dataset
swv_gc_control <- left_join(swv_control_signals %>% filter(run<=3),gc_signals,
by=c('run','rep','reactor','treatment'),
suffix=c('_from_swv','_from_gc'))
write_csv(swv_gc_control,"./01_17_19_swv_gc_control_dap_processed.csv")
ggplot(swv_gc_control %>% filter(reactor=='transfer' & electrode_from_swv=='i1' & rep>0), aes(x = signal_from_swv, y = signal_from_gc, color=factor(run)))+
geom_line()+
geom_point()+
geom_smooth(method='lm',se=F)+
scale_color_viridis(discrete = T)
swv_gc_control_lms <- swv_gc_control %>%
filter(reactor=='transfer' & rep>0 & electrode_from_swv=='i1') %>%
group_by(run,treatment,electrode_from_swv) %>%
do(tidy(lm(signal_from_gc~signal_from_swv,.)))
Psoralen dataset
swv_gc_psoralen <- left_join(swv_psoralen_signals ,gc_signals,
by=c('run','rep','reactor','treatment'),
suffix=c('_from_swv','_from_gc'))
write_csv(swv_gc_psoralen,"./01_17_19_swv_gc_psoralen_dap_processed.csv")
ggplot(swv_gc_psoralen %>% filter(reactor=='transfer' & electrode_from_swv=='i1' & rep>0), aes(x = signal_from_swv, y = signal_from_gc, color=factor(run)))+
geom_line()+
geom_point()+
scale_color_viridis(discrete = T)
swv_gc_psoralen_lms <- swv_gc_psoralen %>%
filter(reactor=='transfer' & rep>0) %>%
group_by(run,treatment,electrode_from_swv) %>%
do(tidy(lm(signal_from_gc~signal_from_swv,.)))


public class NotificationScheduler
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
private android.content.Context |
context |
protected static java.lang.String |
NOTIFICATION_ID_KEY |
protected static java.lang.String |
NOTIFICATION_KEY |
private int |
notificationId |
private int |
RECURRING_MODE_ONE |
private int |
RECURRING_MODE_TWO |
private int |
RECURRING_MODE_ZERO |
private java.lang.String |
RECURRING_NOTIFICATION_CHANNEL |
private int |
recurringMode |
| Constructor and Description |
|---|
NotificationScheduler(android.content.Context context)
Constructor that takes application context.
|
| Modifier and Type | Method and Description |
|---|---|
private java.lang.String |
formatNotificationText(Recurring recurring)
Formats the string that is displayed in the recurring transactions
notification.
|
private long |
getRecurringNotificationAdvanceInMillis(java.util.Date date)
Gets the time in advance according the recurring notification mode of
the specified date in milliseconds.
|
void |
schedule(Recurring recurring)
Schedules a notification to be displayed by the system for the specified
Recurring Transaction.
|
private android.content.Context context
protected static final java.lang.String NOTIFICATION_ID_KEY
protected static final java.lang.String NOTIFICATION_KEY
private int notificationId
private final int RECURRING_MODE_ONE
private final int RECURRING_MODE_TWO
private final int RECURRING_MODE_ZERO
private final java.lang.String RECURRING_NOTIFICATION_CHANNEL
private int recurringMode
public NotificationScheduler(android.content.Context context)
context
- application contextprivate java.lang.String formatNotificationText(Recurring recurring)
recurring
- Recurring transaction objectprivate long getRecurringNotificationAdvanceInMillis(java.util.Date date)
date
- Datepublic void schedule(Recurring recurring)
recurring
-


project_type): ?>
= e($formModel->project_type->name) ?>
Duration = $formModel->duration ?> days
= e(Str::limit($formModel->name, 35)) ?>
Submitted = $formModel->created_at->diffForHumans() ?>
= $formModel->status->name ?>
user): ?>User missing!
status->code == 'pending'): ?>Needs approval
status->code == 'rejected'): ?>Waiting for a response
Everything's OK
Unknown user
This user may have deactivated their account.


{% extends '_layouts/layout.html' %} {% block page_title %}{% endblock %} {% block body_css_classes %}nhsuk-frontend{% endblock %} {% block content %}
We’ll call your number. {{ session.callBooking.name.firstname }} might have to answer some questions during the call.
{{ session.callBooking.tel }}


title = "Home"
url = "/"
layout = "default"
is_hidden = 0
[blogPosts]
pageNumber = "{{ :page }}"
categoryFilter = "news"
postsPerPage = 10
noPostsMessage = "No posts found"
sortOrder = "published_at desc"
categoryPage = 404
postPage = "single-news-post"
[campaignrUpcoming]
sorting = "checked"
evtNumber = 100
eventPage = "single-news-post"
eventSlug = ":slug"
[contactForm]
[contactForm contactFormFooter]
==
{{ 'Information Sharing'|_ }}
{{'Enhance access to Information, expand networks, build understanding and trust ocross sector for increased engagement in community development'|_}}
{{'Capacity Building'|_}}
{{'Empower local NPOs, skilled volunteers, and donors with tools and knowledge to effectively address social challenges and inequalities.'|_}}
{{'Resource Mobilization'|_}}
{{'Leverage community resources: financial, human, shared working space, technology, and networks, for NPOs to fulfill their capacity for sustainable development.'|_}}'
{{'Subscribe to our mailing list to receive updates about our news and events'|_}}


Coming Soon!
Our new
Money Transfer
and
Online Payments
website is on its way.
Get notified when we launch
Subscribe
Copyright © 2020
Payyed
. All Rights Reserved.


--- layout: post title: O2 lead for mobile data date: 2005-10-22 00:00:00 categories: business mobile ---
O2 lead for mobile data: "O2, which itself launched i-mode in October 2005, is the leading European operator for data ARPU. It generated data ARPU of US$11.8 in the UK in Q2 2005. The operator's high data revenues are achieved mainly through to its market leadership in messaging (more than 40% of UK SMS originate from an O2 subscriber)."
Yep - we see a disproportionate amount of traffic to a service which isn't currently promoted via any operator portal, all from O2.


BJP to launch bypoll campaign from tomorrow
CM and Kateel will begin their poll tour from different directions on that day
by Special CorrespondentThe Bharatiya Janata Party (BJP) is set to formally launch its campaign for the bypolls to 15 Assembly constituencies from November 23 with Chief Minister B.S. Yediyurappa and party State president Nalin Kumar Kateel beginning the State tour separately.
While the Chief Minister will begin his poll tour from the northern constituency of Athani, Mr. Kateel will start from the southern constituency of K.R. Pet.
Itinerary released
According to the itinerary released by party State general secretary Arvind Limbavali, Mr. Yediyurappa will address election campaign meetings in three constituencies a day and complete the first round of tour in all the 15 Assembly constituencies on November 27. He will commence his second round from November 28 and finish on December 3.
Mr. Limbavali, who also released the party’s pamphlet for the bypolls, said “political stability” will be the main agenda for the party in all constituencies going to polls. Also, the BJP will try to convince that having the same party governments in the Centre and State was an added advantage, he said.
Micro-management
The BJP is resorting to micro-management in the crucial bypolls to 15 Assembly constituencies. The party on Thursday announced that it would complete the process of formation of 4,185 booth-level committees in the constituencies by Sunday.
The party appears to be using bypolls to strengthen its grassroot-level cadre in the 15 constituencies, which it had lost in 2018, by forming booth-level committees. The party has decided to form separate WhatsApp groups in all 4,185 groups.


Non-blocking I/O for everyone!
Build highly scalable event-oriented network services with a simplified, yet highly configurable, API.
The primary building blocks of the Twuni I/O library are the EventHandler
object and the Connection
object. A connection is writable and stateful, and an event handler is notified whenever a connection is connected, disconnected, when incoming data is ready to be processed, and when an exception occurs.
Typically, you would register a connection with your application in EventHandler#onConnected
, unregister it from your application in EventHandler#onDisconnected
, and forward data to your application for processing in EventHandler#onData
.
If you roll your own ConnectionFactory
and Connection
, then you can attach all sorts of things to your connection which allow its state to be shared throughout the connection's lifespan.
There are some additional helper classes to lay the foundation for some common use cases, such as authentication (Authenticator
and friends) and packet relay (Transporter
and Queue
).


In addition to the data part, messages carry information that can be used to
distinguish messages and selectively receive them. This information consists
of a fixed number of fields, which we collectively call
the message envelope. These fields are
source
destination
tag
communicator
The message source is implicitly determined by the identity of the
message sender. The other fields are specified by arguments in the send
operation.
The message destination is specified by the dest argument.
The integer-valued message tag is specified by the tag argument. This integer can be used by the program to distinguish different types of messages. The range of valid tag values is 0,...,UB, where the value of UB is implementation dependent. It can be found by querying the value of the attribute MPI_TAG_UB, as described in Chapter MPI Environmental Management . MPI requires that UB be no less than 32767.
The comm argument specifies the communicator that is used for the send operation. Communicators are explained in Chapter Groups, Contexts, and Communicators ; below is a brief summary of their usage.
A communicator specifies the communication context for a communication operation. Each communication context provides a separate ``communication universe:'' messages are always received within the context they were sent, and messages sent in different contexts do not interfere.
The communicator also specifies the set of processes that share this communication context. This process group is ordered and processes are identified by their rank within this group. Thus, the range of valid values for dest is 0, ... , n-1, where n is the number of processes in the group. (If the communicator is an inter-communicator, then destinations are identified by their rank in the remote group. See Chapter Groups, Contexts, and Communicators .)
A predefined communicator MPI_COMM_WORLD is provided by MPI. It allows communication with all processes that are accessible after MPI initialization and processes are identified by their rank in the group of MPI_COMM_WORLD.
[] Advice to users.
Users that are comfortable with the notion of a flat name space for processes, and a single communication context, as offered by most existing communication libraries, need only use the predefined variable MPI_COMM_WORLD as the comm argument. This will allow communication with all the processes available at initialization time.
Users may define new communicators, as explained in Chapter Groups, Contexts, and Communicators
.
Communicators provide an important encapsulation mechanism for libraries and
modules. They allow modules to have their own disjoint communication universe
and their own process numbering scheme.
( End of advice to users.)
[] Advice
to implementors.
The message envelope
would normally be encoded by a fixed-length message header.
However, the actual encoding
is implementation dependent. Some of the information (e.g.,
source or destination) may be implicit, and need not be
explicitly carried by messages. Also, processes may be identified by
relative ranks, or absolute ids, etc.
( End of advice to implementors.)


{{provider[0].name}}
{{provider[0].address}}
Facilities and Services
{{provider[0].description}}
{{provider[0].legal_name}}
| Services | Availability | Notes |
|---|---|---|
| Additional Activities | {{provider[0].additional_activities | ngbool }} | {{provider[0].additional_activities_list}} |
| Age Range | {{provider[0].age_range}} | |
| Air Conditioning | {{provider[0].air_conditioning | ngbool }} | |
| Approval Granted | {{provider[0].approval_granted_on | date}} | Date approval to operate was granted. |
| Approval Number | {{provider[0].approval_number}} | |
| Approved Places | {{provider[0].approved_places}} | Number of children approved for {{provider[0].name}}. |
| Bus Service | {{provider[0].bus_service | ngbool }} | |
| Conditions for Approval | {{provider[0].conditions_on_approval}} | Set when approved to enroll children. |
| Cloth Nappies | {{provider[0].cloth_nappies | ngbool }} | |
| Disability Friendly | {{provider[0].disability_friendly | ngbool }} | {{provider[0].disabilities_list}} |
| Cloth Nappies | {{provider[0].cloth_nappies | ngbool }} | |
| Excursions | {{provider[0].excursions | ngbool }} | |
| Extended Hours for Kindys | {{provider[0].extended_hours_for_kindys | ngbool }} | |
| Fee | {{provider[0].fee}} | Normally $/hr. |
| Food Provided | {{provider[0].food_provided | ngbool }} | |
| Guest Speakers | {{provider[0].guest_speakers | ngbool }} | |
| Hours | {{provider[0].hours}} | |
| Kindergarten | {{provider[0].kindergarten | ngbool }} | |
| Languages | {{provider[0].languages | ngbool }} | {{provider[0].languages_list}} |
| NQS Rating | {{provider[0].NQS_rating}} | |
| Online Enrollment | {{provider[0].online_enrollment | ngbool }} | |
| Outdoor Play Area | {{provider[0].outdoor_play_area | ngbool }} | |
| Provider Approval Number | {{provider[0].provider_approval_number}} | |
| Quality Overall Rating | {{provider[0].quality_overall_rating}} | |
| Overall Rating | {{provider[0].quality_overall_rating}} | |
| Real Grass | {{provider[0].real_grass | ngbool }} | |
| Secure Access | {{provider[0].secure_access | ngbool }} | |
| Service Approval Number | {{provider[0].service_approval_number}} | |
| Sibling Priority | {{provider[0].sibling_priority | ngbool }} | |
| Technology | {{provider[0].technology | ngbool }} | {{provider[0].technologies_list}} |
| Type of Care | {{provider[0].care_offered | ngbool }} | |
| URL (Web Site) | {{provider[0].url}} | |
| Vacancies | {{provider[0].vacancies | ngbool }} | {{provider[0].vacancies_list}} |
| Vaccinations Are Compulsory | {{provider[0].vaccinations_compulsory | ngbool }} | |
| Waitlist Fee | {{provider[0].waitlist_fee | currency}} | |
| Waitlist Online | {{provider[0].waitlist_online | ngbool }} | |
| Waitlist Fee Re-imbursed | {{provider[0].waitlist_reimbursed}} | |
| Date Created | {{provider[0].created_at | date}} | Date first appeared in Daycare Decisions. |
| Date Updated | {{provider[0].updated_at | date}} | Most recent change date. |


Thank you for downloading: <HORIZONTAL SCROLL PORTFOLIO V2>
Included are:
- Flash 8
- index.fla
- contact.fla
- music/music_player.fla
- mailform.php
Font(s) used:
- Arial (system font)
- Music player font : uni05_54
How to modify the file:
- portfolio : portfolioSetting.xml in xml folder.(go edit.)
- swf and jpg load : JPG in imagesPortfolio folder. swf( 01.fla) in imagesPortfolio folder.(used 01.fla to increase)
- mailform.php : CHMODE 777 ( ftp server)
Good Luck using the file in your projects! THANKS !


This inspection lets you spot the following problems that might occur in your ejb-jar.xml and other XML descriptors used to deploy
your EJB to a Java EE server:
References to the non-instantiable classes
References to the classes that do not extend required class
References to classes with inappropriate scope
Empty tag and attribute values
Tag and attribute values that do not match required pattern (e.g. Java Identifiers)
Tags that do not include required children tags or attributes
Tags that define objects with duplicate names


Two major categories of faults, namely transient and permanent faults, lead to unreliability of a design[3]. Transient faults manifest into soft errors which temporarily corrupt the data or the output of a combinational circuit. On the other hand, permanent faults lead to permanent damage of the circuit and manifest as hard errors. Permanent faults are caused by extrinsic sources to the design such as manufacturing defects, process variation or intrinsic sources, which are caused by wearing out of the design material.
The impact of faults can be investigated through simulation. While faults can be simulated accurately only at the circuit level of abstraction, many proposals exist to inject the fault at high level of abstraction for early reliability exploration. Pure software fault injection techniques alter the processor state (memory, register) to simulate a fault, however, it suffers from a restricted view of the micro-architecture. Register-Transfer Level (RTL) and gate-level fault injection approaches [4] simulate the hardware behavior with more accuracy and therefore, are usually slow and in some cases need repeated compilation. In order to hit a balance, fault injection can be performed during instruction-set simulation (ISS) [5], [6], which offers different degrees of accuracy and speed trade-off. In [7], similar accuracies are achieved for ISS and RTL fault injection. Cho [8] presents a quantitative evaluation on the accuracy of different fault injection techniques by tracking error propagation using FPGA-based emulation system, where the inaccuracy analysis is conducted through exhaustive fault injection experiments.
In contrast to the simulation techniques, analytical methods have also been proposed to investigate fault tolerant behavior of circuits. Mukherjee et al. [9] introduced the concept of architecturally correct execution (ACE) to compute the vulnerability factors of faulty architecture components. In [10], ACE analysis is performed to compute architectural vulnerability factors for cache and buffers. Reliability-aware software transformations are proposed by Rehman et al. [11,12] which used the ACE to instruction vulnerability analysis. The vulnerability of the instruction is analyzed by investigating the constituent logic blocks. While the instruction vulnerability index model proposed in [11] includes the logical masking effects, the details of derivation of the masking effect are not mentioned. Wang [13] analyze application error rates using instruction vulnerability combined with instruction profiling. Generally, analytical methods suffer from estimation accuracy.
Even with reliability evaluation techniques, design of reliable system in presence of faults, is still a challenging problem. Designing a reliable processor requires thorough understanding of all the causes of failures such as external radiation, electromigration and thermal cycles. Furthermore, reliability brings forth trade-off with other design dimensions. First, reliability can be traded off with area via hardware redundancy. Second, energy and reliability are related via power management and thermal cycling. Besides, performance is affected when reliability requires conservative frequency allocation. This prompted multiple processor design proposals allowing dynamic trade-off between different design constraints taking reliability into account [14,15,16,17].
In recent research reliability is treated as a cross-layer design issue [18]. This stresses the fact that separate error mitigation techniques from individual design abstractions may result in an over-protected system. The design should take the support of architectural and algorithmic error resilience [19]. For instance, approximate designs in circuit-level indicates huge power saving [20]. Algorithmic level error resilience is also frequently observed in image and video processing algorithms, where errors can be tolerated due to limited human perception. In such fields approximate values are sufficient against accurate ones [21], so that Quality-Of-Service (QoS) can be compromised to trade-off other metrics. Huge benefits can be gained from the field of approximate computation. However, it requires strong understanding of the fault propagation through design layers which is extremely difficult to predict the effects of errors through purely fault injection or analytical reliability models.
In particular approximate error prediction is an issue when algorithmic reliability is explored or when inexact, probabilistic computing [22] is performed. Similar research was pursued earlier for floating-to-fix point conversion in DSP design [23]. However, there the error localities were restricted to variables (sizes of fixed points) and operators (saturation, rounding effects) without any architecture-level concern. Krishnaswamy et al. proposed a framework called Probabilistic Transfer Matrix (PTM) [24] which captures the probabilistic behavior of the circuit while estimates the approximate error probability of faults inside the circuit. Analytical study of error propagation could be potentially addressed using PTM. However, PTM suffers from scalability problem for large design due to its bit-level accuracy and is not initially designed for handling error masking effects.
In this work we first develop the concept of a new algebraic representation named Probabilistic error Masking Matrix (PeMM) to investigate the masking effects on input errors of the circuits. Compared with PTM, PeMM has a reduced calculation complexity due to the scope of error focusing on coarse-grained signal level. Next we integrate PeMM algebra into a high-level processor design framework and represent logic error as an abstract data structure called token. An automated analysis flow is presented where the token propagation can be accurately predicted using cycle-accurate instruction-set simulator while the error masking effects are carefully addressed using PeMM for individual micro-architecture unit. Fine-grained PeMM is also proposed which calculates error nibble or byte-wise probabilities on data signals. Consequently, the significance of logic faults through design abstractions could be approximately predicted in earlier design phases.
The rest of the paper is organized as following. Section II illustrates the PeMM algebra in details. Section III introduces the approximate error prediction framework in a high-level processor design environment. Section IV shows the usability of proposed framework using several embedded applications on an RISC processor. The paper is concluded and future work is outlined in section V.


- But I must explain
- To you how all this mistaken
- Idea of denouncing pleasure
- Great explorer of the truth
- To take a trivial example
- Or one who avoids a pain
- Indignation and dislike men
- Nor again is there anyone
- But who has any right
- That they cannot foresee
- Who avoids a pain that produces
- Consequences that are extremely


SEC sides with Bengzon in The Medical City takeover
(UPDATED) The Securities and Exchange Commission says Jose Xavier Gonzales' companies misled the board and omitted facts to creep their way into gaining control of The Medical City
by Ralf RivasSubscribe
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When designing components, following general rules applies.
Give the least possibilities to the developer to change the component. As soon as you give them some freedom, they will take it and transform the logic of the component.
For instance, imagine a button
component.
You accept a <slot>
for the content of the component.
Also, you accept a class
attribute so the developer can style the button as s·he wants.
What will happen?
Some structure will be duplicated (ex: button with icon, colors, sizes) and not always the same way. Even though some developers will try to factorize button styles, there will always a case where it is not done right. When you would want to change some style, you will end up changing the whole application.
To avoid this case, the only thing to do is to restrict the developer.
And by doing so, you also force the designer to be aware of the restriction. When the developer will have an impossibility to make changes without changing the component, it will be an alert to check whether the design is coherent with the existing. And if so, the component should be rethought.
Only when an abstraction can be made and a restrictive one, you can update the component to accept this abstraction.
For example, our button can be an icon-button
but the icon
name can be given by the
parent.
We use a lot of css-variables. Not because they are cool (even if they are) but because they help to define several stylesheets.
For instance, if you want to see with your designer the configuration of the component,
you can use a simple UI-kit like uikit.html
.
Also, if you want to define several stylesheets (day/night modes, accessibility mode, …),
it will be easier to have all your variables in a global definition.
Of course, these exist for specific use case and can be simplified or extended depending on your own needs. They mainly exist for demonstration purpose.
In this page, you will surely think that each component have lots of configurations (and too many). The reason behind it it to show the developer and designer all cases to take into account. In your final application, maybe you will only make little variables configurable.
This repository exists to help you create your UI-kit, so the final components should be simpler in terms of configuration.
However, if you need several stylesheets (eg: night mode), please take into account all cases too.


Help for kids in sports
by Chelsea PowrieThe annual KidSport charitable campaign has launched in Penticton.
"Give the Gift of Sport" has run for eight years now, helping kids in the community get involved with organized sports.
Over 30 per cent of Canadian youth under the age of 18 cannot afford to play organized sport.
With the help of Canadians across the country, the campaign aims to get more kids off the sidelines, into the game, and experience the gift of sport.
“As the 20th year of KidSport Penticton winds down, the committee is looking to gain some ground in 2020,” said committee chair Dave Lieskovsky. “Although 2019 proved to be successful in providing support to kids for sport, the need continues to surpass the funds available.”
An average donation of $250 can provide one child with a full season of sport. KidSport is hoping to raise $250,000 nationally this year.
To learn more and to donate click here, for information about the campaign which wraps up Jan. 6, 2020.


--- layout: default title: Ada Journey description: Learning Ada language ---
The Department of Defense of the United States designed Ada Language for safety critical embedded systems. It has its requirements documents.
In this section, you'll learn together with us.
1. How To Start.
2. Learn The Tools.
3. Typical Project Structure.
4. Secure Coding.
Thanks to Daniel Stori from turnoff.us for allowing us to use his work.