Consolidated storm prediction for aviation (CoSPA)

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

Download Full-text

Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments

Weather and Forecasting ◽

10.1175/waf-d-18-0024.1 ◽

2019 ◽

Vol 34 (1) ◽

pp. 61-79 ◽

Cited By ~ 9

Author(s):

Rebecca D. Adams-Selin ◽

Adam J. Clark ◽

Christopher J. Melick ◽

Scott R. Dembek ◽

Israel L. Jirak ◽

...

Keyword(s):

Threshold Values ◽

Severe Storms ◽

Object Based ◽

Storm Prediction ◽

Horizontal Grid ◽

Grid Based ◽

Hazardous Weather

Abstract Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.

Download Full-text

The Impact of the Storm Prediction Center's Convective Outlooks and Watches on Emergency Management Operational Planning

Journal of Operational Meteorology ◽

10.15191/nwajom.2021.0903 ◽

2021 ◽

pp. 36-46

Author(s):

Heather A. Cross ◽

Dennis Cavanaugh ◽

Christopher C. Buonanno ◽

Amy Hyman

Keyword(s):

Emergency Management ◽

Severe Weather ◽

Emergency Operations ◽

Risk Levels ◽

Severe Thunderstorms ◽

Emergency Managers ◽

Weather Events ◽

Storm Prediction ◽

Near Term ◽

The Impact

For many emergency managers (EMs) and National Weather Service (NWS) forecasters, Convective Outlooks issued by the Storm Prediction Center (SPC) influence the preparation for near-term severe weather events. However, research into how and when EMs utilize that information, and how it influences their emergency operations plan, is limited. Therefore, to better understand how SPC Convective Outlooks are used for severe weather planning, a survey was conducted of NWS core partners in the emergency management sector. The results show EMs prefer to wait until an Enhanced Risk for severe thunderstorms is issued to prepare for severe weather. In addition, the Day 2 Convective Outlook serves as the threshold for higher, value-based decision making. The survey was also used to analyze how the issuance of different risk levels in SPC Convective Outlooks impact emergency management preparedness compared to preparations conducted when a Convective Watch is issued.

Download Full-text

Evaluation of the Storm Prediction Center’s Convective Outlooks from Day 3 through Day 1

Weather and Forecasting ◽

10.1175/waf-d-13-00132.1 ◽

2014 ◽

Vol 29 (5) ◽

pp. 1134-1142 ◽

Cited By ~ 14

Author(s):

Nathan M. Hitchens ◽

Harold E. Brooks

Keyword(s):

Lead Times ◽

Annual Basis ◽

Long Period ◽

Target Period ◽

Storm Prediction

Abstract The Storm Prediction Center issues four categorical convective outlooks with lead times as long as 48 h, the so-called day 3 outlook issued at 1200 UTC, and as short as 6 h, the day 1 outlook issued at 0600 UTC. Additionally, there are four outlooks issued during the 24-h target period (which begins at 1200 UTC on day 1) that serve as updates to the last outlook issued prior to the target period. These outlooks, issued daily, are evaluated over a relatively long period of record, 1999–2011, using standard verification measures to assess accuracy; practically perfect forecasts are used to assess skill. Results show a continual increase in the skill of all outlooks during the study period, and increases in the frequency at which these outlooks are skillful on an annual basis.

Download Full-text

Conversion of Meteorological Input Data Implemented in the Algorithm of Storm Prediction

Advances in Intelligent Systems and Computing - Cybernetics and Automation Control Theory Methods in Intelligent Algorithms ◽

10.1007/978-3-030-19813-8_37 ◽

2019 ◽

pp. 357-367

Author(s):

David Šaur ◽

Jaromír Švejda ◽

Roman Žák

Keyword(s):

Input Data ◽

Meteorological Input ◽

Storm Prediction

Download Full-text

Forecasting of flash floods by Algorithm of Storm Prediction

MATEC Web of Conferences ◽

10.1051/matecconf/201821004033 ◽

2018 ◽

Vol 210 ◽

pp. 04033 ◽

Cited By ~ 2

Author(s):

David Šaur ◽

Kateřina Víchová

Keyword(s):

Prediction Models ◽

Preventive Measures ◽

Weather Prediction ◽

Flash Floods ◽

Convective Precipitation ◽

Result Section ◽

Rescue Service ◽

Weather Events ◽

Storm Prediction ◽

Numerical Weather Prediction Models

This article focuses on the forecasting of flash floods using the Algorithm of Storm Prediction as a new tool to predict convective precipitation, severe phenomena and the risk of flash floods. The first part of the article contains information on methods for predicting dangerous severe phenomena. This algorithm uses mainly data from numerical weather prediction models (NWP models), database of historic weather events and relief characteristics describing the influence of orography on the initiation of atmospheric convection. The result section includes verification of predicted algorithm outputs, selected NWP models and warnings of CHMI and ESTOFEX on three events related to the floods that hit the Zlín Region between years of 2015 - 2017. The main result is a report with prediction outputs of the algorithm visualized in maps for the territory of municipalities with extended competence and their regions. The outputs of the algorithm will be used primarily to increase the effectiveness of preventive measures against flash floods not only by the Fire Rescue Service of Czech Republic but also by the flood and crisis management authorities.

Download Full-text