scholarly journals Transforming Satellite Data into Weather Forecasts

Eos ◽  
2017 ◽  
Author(s):  
Emily Berndt ◽  
Andrew Molthan ◽  
William Vaughan ◽  
Kevin Fuell
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Next Generation ◽  
Weather Forecasts ◽  
Weather Forecasters

A NASA project spans the gap between research and operations, introducing new composites of satellite imagery to weather forecasters to prepare for the next generation of satellites.

International competency requirements for public weather forecasters /

2021 ◽  
Vol 3 ◽  
pp. 161-171
Author(s):  
E.V. Vasil’ev ◽  
Keyword(s):  
Satellite Data ◽  
Weather Forecasting ◽  
World Meteorological Organization ◽  
Competency Assessment ◽  
Further Training ◽  
Knowledge And Skills ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
The World

Competency requirements for public weather forecasters, as well as knowledge and skills necessary for their implementation, that were developed and recommended for practical use by the World Meteorological Organization, are presented. Basic skills of working with radar and satellite data are described. The importance of the weather forecaster competency compliance with the presented requirements is emphasized, as well as a need for proper competency assessment and, if necessary, further training in order to improve the quality of weather forecasts and storm warnings. Keywords: competency, weather forecasters, weather forecasting, knowledge and skills, competency assessment, training

International competency requirements for public weather forecasters

2021 ◽  
Vol 3 ◽  
pp. 162-171
Author(s):  
E.V. / Vasil’ev ◽  
Keyword(s):  
Satellite Data ◽  
Weather Forecasting ◽  
World Meteorological Organization ◽  
Competency Assessment ◽  
Further Training ◽  
Knowledge And Skills ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
The World

Competency requirements for public weather forecasters, as well as knowledge and skills necessary for their implementation, that were developed and recommended for practical use by the World Meteorological Organization, are presented. Basic skills of working with radar and satellite data are described. The importance of the weather forecaster competency compliance with the presented requirements is emphasized, as well as a need for proper competency assessment and, if necessary, further training in order to improve the quality of weather forecasts and storm warnings. Keywords: competency, weather forecasters, weather forecasting, knowledge and skills, competency assessment, training

scholarly journals Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

2021 ◽  
Vol 893 (1) ◽  
pp. 012040
Author(s):  
Immanuel Jhonson Arizona Saragih ◽  
Huda Abshor Mukhsinin ◽  
Kerista Tarigan ◽  
Marzuki Sinambela ◽  
Marhaposan Situmorang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Initial Data ◽  
Satellite Data ◽  
Wrf Model ◽  
Heavy Rain ◽  
Convective System ◽  
Large Area ◽  
Weather Forecasts ◽  
Rain Events ◽  
North Sumatra

Abstract Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

Kerch Strait and Its Technogenic Pollution: Possibilities of Optical and Radar Remote Sensing

2021 ◽  
Vol 25 (8) ◽  
pp. 21-27
Author(s):  
A.Yu. Ivanov ◽  
D.V. Khlebnikov ◽  
B.V. Konovalov ◽  
S.K. Klimenko ◽  
N.V. Terleeva
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Marine Environment ◽  
Satellite Data ◽  
Emergency Situation ◽  
Anthropogenic Pollution ◽  
Complete Information ◽  
Technogenic Pollution ◽  
Radar Remote Sensing ◽  
Kerch Strait

The possibilities of using satellite imagery of modern remote sensing satellites, both optical and radar, to study anthropogenic pollution and the state of the marine environment of the Kerch Strait are discussed. It is shown that satellite data and images allow one to quickly obtain practically complete information about a particular phenomenon and emergency situation in the strait.

scholarly journals Ice charting based on multispectral satellite data in the Baltic Sea

1994 ◽  
Vol 19 ◽  
pp. 121-125
Author(s):  
V.A. Golovko ◽  
M. Leppäranta ◽  
S. Kalliosaari ◽  
YU.S. Sedunov ◽  
A.M. Volkov
Keyword(s):  
Cluster Analysis ◽  
Image Segmentation ◽  
Satellite Imagery ◽  
Satellite Data ◽  
The Baltic Sea ◽  
Satellite Systems ◽  
Optical Scanner ◽  
Operational Space ◽  
The Baltic ◽  
Operational Data

Results are presented from an experiment concerning operational space-borne ice charting based on the Russian Ocean and Resource satellite systems. The surface truth consisted of routine operational data, helicopter-borne reconnaissance, and some ground measurements. Examples of the satellite imagery are given and identification of ice types is described. Cluster-analysis has been used for automatic image segmentation. The potential of these satellites in operational ice charting is discussed. A 160 m resolution optical scanner and a 2 km resolution radar are found to be very useful complements to the present routine system.

Weather Forecasting by Humans—Heuristics and Decision Making

2004 ◽  
Vol 19 (6) ◽  
pp. 1115-1126 ◽  
Author(s):  
Charles A. Doswell
Keyword(s):  
Decision Making ◽  
Cognitive Psychology ◽  
Multidisciplinary Approach ◽  
Weather Forecasting ◽  
Judgment And Decision Making ◽  
Human Cognition ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
Insight Into ◽  
Considerable Insight

Abstract The decision-making literature contains considerable information about how humans approach tasks involving uncertainty using heuristics. Although there is some reason to believe that weather forecasters are not identical in all respects to the typical subjects used in judgment and decision-making studies, there also is evidence that weather forecasters are not so different that the existing understanding of human cognition as it relates to making decisions is entirely inapplicable to weather forecasters. Accordingly, some aspects of cognition and decision making are reviewed and considered in terms of how they apply to human weather forecasters, including biases introduced by heuristics. Considerable insight into human forecasting could be gained by applying available studies of the cognitive psychology of decision making. What few studies exist that have used weather forecasters as subjects suggest that further work might well be productive in terms of helping to guide the improvement of weather forecasts by humans. It is concluded that a multidisciplinary approach, involving disciplines outside of meteorology, needs to be developed and supported if there is to be a future role for humans in forecasting the weather.

scholarly journals Snowfall Rates from Satellite Data Help Weather Forecasters

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Ralph Ferraro ◽  
Huan Meng ◽  
Brad Zavodsky ◽  
Sheldon Kusselson ◽  
Deirdre Kann ◽  
...  
Keyword(s):  
Satellite Data ◽  
Weather Data ◽  
Data Product ◽  
Weather Forecasters

A new data product calculates snowfall rates from weather data beamed directly from several satellites, helping meteorologists provide fast, accurate weather reports and forecasts.

scholarly journals Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

2020 ◽  
Vol 35 (4) ◽  
pp. 1605-1631
Author(s):  
Eric D. Loken ◽  
Adam J. Clark ◽  
Christopher D. Karstens
Keyword(s):  
Random Forests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Decision Makers ◽  
Limited Information ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Weather Forecasters ◽  
Forecast Data ◽  
Better Than

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

scholarly journals Towards Objective Identification and Tracking of Convective Outflow Boundaries in Next-Generation Geostationary Satellite Imagery

2019 ◽  
Author(s):  
Jason M. Apke ◽  
Kyle A. Hilburn ◽  
Steven D. Miller ◽  
David A. Peterson
Keyword(s):  
Optical Flow ◽  
Satellite Imagery ◽  
Numerical Models ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Primary Source ◽  
False Alarms ◽  
Identification Algorithm ◽  
Next Generation ◽  
Rapid Variation

Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal and spatial resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite (GOES)-16 imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery, and back-tracking them using optical flow to determine if they originated from a deep convection source. This objective OFB identification is tested with a case study of an OFB triggered dust storm over southern Arizona. Results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

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