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SatFC-G: Impact of Satellite Observations on NWP
This lesson covers how satellite data inform numerical weather prediction models. From a basic overview of how satellite data is assimilated to how a new instrument's data might get into a model. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth discussions and a quiz on the impacts of satellite observations on NWP can be found in the COMET lesson, How Satellite Observations Impact NWP.
Available online: https://www.meted.ucar.edu/training_module.php?id=1258
Published by: The University Corporation for Atmospheric Research ; 2016
This lesson covers how satellite data inform numerical weather prediction models. From a basic overview of how satellite data is assimilated to how a new instrument's data might get into a model. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth discussions and a quiz on the impacts of satellite observations on NWP can be found in the COMET lesson, How Satellite Observations Impact NWP.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Data assimilation ; Forecast error ; Lesson/ Tutorial ; Satellite Skills and Knowledge for Operational Meteorologists
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How Satellite Observations Impact NWP
Satellite observations have a huge impact on numerical weather prediction (NWP) model analyses and forecasts, with sounding data from polar orbiting and GPS-radio occultation satellites reducing model forecast error by almost half. All of this despite the fact that NWP models only assimilate 5% of all satellite observations! This lesson discusses the use of satellite observations in NWP and how model limitations prevent more of the data from being assimilated. The lesson begins by briefly describing the history of satellite observations in NWP and their impact on NWP model forecast skill. The ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1016
Published by: The University Corporation for Atmospheric Research ; 2014
Satellite observations have a huge impact on numerical weather prediction (NWP) model analyses and forecasts, with sounding data from polar orbiting and GPS-radio occultation satellites reducing model forecast error by almost half. All of this despite the fact that NWP models only assimilate 5% of all satellite observations! This lesson discusses the use of satellite observations in NWP and how model limitations prevent more of the data from being assimilated. The lesson begins by briefly describing the history of satellite observations in NWP and their impact on NWP model forecast skill. The next part provides background information about the types of environmental satellites that provide input to NWP, the satellite observations that are assimilated, the major components of NWP models, and how they forecast atmospheric behavior. This sets the stage for the main part of the lesson, which examines how observations from new satellite instruments are vetted for inclusion in data assimilation systems and how observations deemed acceptable are actually assimilated. The final part describes current challenges to making optimal use of satellite observations in NWP and advances that are expected to address these challenges and improve model forecasts.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; Data assimilation ; Forecast error ; NWP Skills and Knowledge for Operational Meteorologists ; Satellite Skills and Knowledge for Operational Meteorologists
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WWRP, 2012-1. Recommended Methods for Evaluating Cloud and Related Parameters
World Meteorological Organization (WMO) ; WWRP/WGNE Joint Working Group on Forecast Verification Research (JWGFVR) - WMO, 2012Cloud errors can have wide-reaching impacts on the accuracy and quality of outcomes, most notably, but not exclusively, on temperature. This is especially true for weather forecasting, where cloud cover has a significant impact on human comfort and wellbeing. Whilst public perception may not be interested in absolute precision, i.e. whether there were 3 or 5 okta of cloud, there is anecdotal evidence to suggest strong links between the perceptions of overall forecast accuracy and whether the cloud was forecast correctly, mostly because temperature errors often go hand-in-hand. It is therefore ...
World Meteorological Organization (WMO) ; WWRP/WGNE Joint Working Group on Forecast Verification Research (JWGFVR)
Published by: WMO ; 2012Cloud errors can have wide-reaching impacts on the accuracy and quality of outcomes, most notably, but not exclusively, on temperature. This is especially true for weather forecasting, where cloud cover has a significant impact on human comfort and wellbeing. Whilst public perception may not be interested in absolute precision, i.e. whether there were 3 or 5 okta of cloud, there is anecdotal evidence to suggest strong links between the perceptions of overall forecast accuracy and whether the cloud was forecast correctly, mostly because temperature errors often go hand-in-hand. It is therefore not surprising that forecasting cloud cover is one of the key elements in any public forecast, although the priority is dependent on the local climatology of a region. Forecasting cloudiness accurately remains one of the major challenges in many parts of the world. There are more demanding customers of cloud forecasts, notably the aviation sector, to name but one in particular, which has strict cloud-related safety guidelines. For example, Terminal Aerodrome Forecasts (TAFs) are a key component of airfield operations, although even now most of these are still manually compiled, and do not contain raw model forecasts. [...]
Collection(s) and Series: WWRP- No. 2012-1
Language(s): English
Format: Digital (Free), Hard copy (ill., charts)Tags: Observations ; Guidelines ; Cloud ; Weather forecasting ; Forecast error ; Synoptic meteorology
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in ECMWF Newsletter > Number 128 (Summer 2011) . - p.17-22Language(s): English
Format: DigitalTags: Observations ; Satellite ; Forecast error
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in ECMWF Newsletter > Number 128 (Summer 2011) . - p.23-27Language(s): English
Format: DigitalTags: Observations ; Cloud ; Forecast error
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A Comparison Study of the Contributions of Additional Observations in the Sensitive Regions Identified by CNOP and FSV to Reducing Forecast Error Variance for the Typhoon Morakot
The sensitive regions of conditional nonlinear optimal perturbations (CNOPs) and the first singular vector (FSV) for a northwest Pacific typhoon case are reported in this paper. A large number of probes have been designed in the above regions and the ensemble transform Kalman filter (ETKF) techniques are utilized to examine which approach can locate more appropriate regions for typhoon adaptive observations. The results show that, in general, the majority of the probes in the sensitive regions of CNOPs can reduce more forecast error variance than the probes in the sensitive regions of FSV. Thi ...
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GCOS, 127. Conseil Pratique pour l'Établissement des Messages CLIMAT
Organisation météorologique mondiale (OMM); Programme des Nations Unies pour l'environnement (PNUE); Conseil International pour la Science (ICSU); et al. - OMM, 2009
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GCOS, 127. Practical Help for Compiling CLIMAT Reports
World Meteorological Organization (WMO) ; United Nations Environment Programme (UNEP); International Council for Science (ICSU); et al. - WMO, 2009 (WMO/TD-No. 1477)
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ГСНК, 127. Практическая помощь в составлении сводок CLIMAT
Всемирная Метеорологическая Организация (BMO); Программа ООН по окружающей среде (ЮНЕП); Международного совета по науке (ICSU); et al. - BMO, 2009 (ВMO/TД-No. 1477)
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SMOC, 127. Ayuda práctica para la compilación de informes CLIMAT
Organización Meteorológica Mundial (OMM); Programa de Naciones Unidas para el Medio Ambiente (PNUMA); Consejo Internacional para la Ciencia (ICSU); et al. - OMM, 2009 (OMM/DT (ES)-No. 1477)
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Operational hydrology report (OHR), 21. Methods of correction for systematic error in point precipitation measurement for operational use
This publication, in the form of an operational manual, describes methods of correcting errors with diagrams, formulae, a detailed chronology and over 100 references. The long-term aim is the wider acceptance and use of internationally recognized methods of correcting precipitation data.
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Problems in dynamic meteorology
World Meteorological Organization (WMO) ; Gandin L.S.; Danovich A.M.; et al. - WMO, 1970 (WMO-No. 261)
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