scholarly journals Assimilation of F-16 Special Sensor Microwave Imager/Sounder Data in the NCEP Global Forecast System

2012 ◽  
Vol 27 (3) ◽  
pp. 700-714 ◽  
Author(s):  
Banghua Yan ◽  
Fuzhong Weng
Keyword(s):  
Standard Deviation ◽  
Weather Prediction ◽  
Gaussian Function ◽  
Atmospheric Temperature ◽  
Naval Research ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Microwave Imager

Abstract The Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F-16 satellite is the first conically scanning sounding instrument that provides information on atmospheric temperature and water vapor profiles. The SSMIS data were preprocessed by the Naval Research Laboratory (NRL) using its Unified Preprocessor Package (UPP) and then distributed to the numerical weather prediction centers by the Fleet Numerical Meteorology and Oceanography Center (FNMOC). This dataset was assimilated into the Global Forecast System (GFS) using gridpoint statistical interpolation (GSI). The initial assimilation of the SSMIS data into the GFS did not improve the medium-range (5–7 days) forecast skill. The SSMIS bias (O-B) still changes with location and time after the GSI bias-correction scheme is implemented. This bias characteristic is related to residual calibration errors in the correction of the SSMIS antenna emission and warm target contamination. The large O-B standard deviation is probably due to the large instrument noise in the SSMIS UPP data. The large O-B and its standard deviation for several surface sensitive channels are also caused by uncertainty in surface emissivity. In this study, a new scheme is developed to remove regionally dependent bias using a weekly composite O-B. The SSMIS noise is reduced through a Gaussian function filter. A new emissivity database for snow and sea ice is developed for the SSMIS surface sensitive channels. After applying these algorithms, the quality of the SSMIS low-atmospheric sounding (LAS) data is improved; the surface-sensitive channels can be effectively assimilated, and the impacts of SSMIS LAS data on the medium-range forecast in the GFS are positive and similar to those from Advanced Microwave Sounding Unit-A (AMSU-A) data.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

scholarly journals Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study

2020 ◽  
Vol 12 (4) ◽  
pp. 670 ◽  
Author(s):  
Xiaowei Jiang ◽  
Jun Li ◽  
Zhenglong Li ◽  
Yunheng Xue ◽  
Di Di ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Global Forecast System ◽  
Forecast System ◽  
Water Vapor Absorption ◽  
Polar Orbit ◽  
Vapor Absorption ◽  
Close Relationship ◽  
New Generation

The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Research and Forecasting (WRF)—are evaluated with brightness temperature (BT) measurements from the three moisture channels of Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite for Typhoon Linfa (2015) case. It is found that the three NWP models have similar performance for lower tropospheric moisture, and GFS has a smaller bias for middle tropospheric moisture. Besides, there is a close relationship between moisture forecasts in the environment and the tropical cyclone (TC) track forecasts in GFS, while regional WRF does not show this pattern. When the infrared and microwave sounder radiance measurements from polar orbit satellite are assimilated in regional WRF, it is clearly shown that the environment moisture fields are improved compared with that with only conventional data are assimilated.

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System

2016 ◽  
Vol 144 (2) ◽  
pp. 643-661 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Takemasa Miyoshi ◽  
Eugenia Kalnay
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Cumulative Distribution ◽  
Precipitation Data ◽  
Global Forecast System ◽  
Low Resolution ◽  
Forecast System ◽  
Original Solution ◽  
Precipitation Analysis ◽  
Ncep Global Forecast System

Abstract Current methods of assimilation of precipitation into numerical weather prediction models are able to make the model precipitation become similar to the observed precipitation during the assimilation, but the model forecasts tend to return to their original solution after a few hours. To facilitate the precipitation assimilation, a logarithm transformation has been used in several past studies. Lien et al. proposed instead to assimilate precipitation using the local ensemble transform Kalman filter (LETKF) with a Gaussian transformation technique and succeeded in improving the model forecasts in perfect-model observing system simulation experiments (OSSEs). In this study, the method of Lien et al. is tested within a more realistic configuration: the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecast System (GFS). With guidance from a statistical study comparing the GFS model background precipitation and the TMPA data, some modifications of the assimilation methods proposed in Lien et al. are made, including 1) applying separate Gaussian transformations to model and to observational precipitation based on their own cumulative distribution functions; 2) adopting a quality control criterion based on the correlation between the long-term model and observed precipitation data at the observation location; and 3) proposing a new method to define the transformation of zero precipitation that takes into account the zero precipitation probability in the background ensemble rather than the climatology. With these modifications, the assimilation of the TMPA precipitation data improves both the analysis and 5-day model forecasts when compared with a control experiment assimilating only rawinsonde data.

The role of arctic forecast errors in the evolution of northern extra-tropical forecast skill

2020 ◽  
Author(s):  
Thomas Haiden
Keyword(s):  
Forecast Error ◽  
Weather Prediction ◽  
Polar Vortex ◽  
Forecast Skill ◽  
The Arctic ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Medium Range Forecast ◽  
Medium Range

&lt;p&gt;&lt;br&gt;Increases in extra-tropical numerical weather prediction (NWP) skill over the last decades have been well documented. The role of the Arctic, defined here as the area north of 60N, in driving (or slowing) this improvement has however not been systematically assessed. To investigate this question, spatial patterns of changes in medium-range forecast error of ECMWF&amp;#8217;s Integrated Forecast System (IFS) are analysed both for deterministic and ensemble forecasts. The robustness of these patterns is evaluated by comparing results for different parameters and levels, and by comparing them with the respective changes in ERA5 forecasts, which are based on a &amp;#8216;frozen&amp;#8217; model version. In this way the effect of different atmospheric variability on the estimation of skill improvement can be minimized. It is shown to what extent the strength of the polar vortex as measured by the Arctic and North-Atlantic Oscillation (AO, NAO) influences the magnitude of forecast errors. Results may indicate whether recent and future changes in these indices, possibly driven in part by sea-ice decline, could systematically affect the longer-term evolution of medium-range forecast skill.&lt;/p&gt;

scholarly journals Changes to the 1995 NCEP Operational Medium-Range Forecast Model Analysis–Forecast System

1997 ◽  
Vol 12 (3) ◽  
pp. 581-594 ◽  
Author(s):  
Peter Caplan ◽  
John Derber ◽  
William Gemmill ◽  
Song-You Hong ◽  
Hua-Lu Pan ◽  
...  
Keyword(s):  
Forecast Model ◽  
Model Analysis ◽  
Forecast System ◽  
Medium Range Forecast ◽  
Medium Range

scholarly journals Evaluation of precipitation forecasts from NOAA global forecast system in hydropower operation

2010 ◽  
Vol 13 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Huicheng Zhou ◽  
Guolei Tang ◽  
Ningning Li ◽  
Feng Wang ◽  
Yajun Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Hydropower Station ◽  
Forecast Errors ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range ◽  
Hydropower Operation ◽  
Yalong River ◽  
Yalong River Basin

Forecasts of 10-day average inflow into the Ertan hydropower station of the Yalong river basin are needed for seasonal hydropower operation. Medium-range inflow forecasts have usually been obtained by Auto-Regressive-Moving-Average (ARMA) models, which do not utilize any precipitation forecasts. This paper presents a simple GFS-QPFs-based rainfall - runoff model (GRR) using the 10-day accumulated Quantitative Precipitation Forecasts from the Global Forecast System (GFS-QPFs) run at the American National Oceanic and Atmospheric Administration (NOAA). In this study, 10-day accumulated GFS-QPFs over the Yalong river basin are verified by first using a three-category contingency table. Then this paper presents the results from a proposed hydrological model using 10-day accumulated GFS-QPFs. Results show that inflow forecast errors can be reduced considerably, compared with those from the currently used ARMA model by both quantitative and qualitative analysis. Finally, simulations of medium-range hydropower operation are also presented using the historical data and forecasts of 10-day average inflows into the Ertan dam during May to September 2006 to evaluate the efficiency of the proposed hydrological model using the GFS-QPFs. The simulations demonstrate that the use of GFS-QPFs has improved reservoir inflow predictions and hydropower operation of the Ertan hydropower station in the Yalong river basin during the wet season.

scholarly journals The Second–Generation Global Forecast System at the Central Weather Bureau in Taiwan

1997 ◽  
Vol 12 (3) ◽  
pp. 653-663 ◽  
Author(s):  
Chi–Sann Liou ◽  
Jen–Her Chen ◽  
Chuen–Teyr Terng ◽  
Feng–Ju Wang ◽  
Chin–Tzu Fong ◽  
...  
Keyword(s):  
Second Generation ◽  
Weather Prediction ◽  
Forecast Model ◽  
Cumulus Parameterization ◽  
Height Anomaly ◽  
Global Forecast System ◽  
Shallow Convection ◽  
Forecast System ◽  
Weather Bureau ◽  
Central Weather Bureau

Abstract The global forecast system (GFS), which started its operation in 1988 at the Central Weather Bureau in Taiwan, has been upgraded to incorporate better numerical methods and more complete parameterization schemes. The second-generation GFS uses multivariate optimum interpolation analysis and incremental nonlinear normal-mode initialization to initialize the forecast model. The forecast model is a global primitive equation model with a resolution of 18 sigma levels in the vertical and 79 waves of triangular truncation in the horizontal. The forecast model includes a 1.5-order eddy mixing parameterization, a gravity wave drag parameterization, a shallow convection parameterization, a relaxed version of Arakawa–Schubert cumulus parameterization, grid-scale condensation calculation, and longwave and shortwave radiative transfer calculations with consideration of fractional clouds. The performance of the second-generation GFS is significantly better than the first-generation GFS. For two 3-month periods in winter 1995/96 and summer 1996, the second-generation GFS provided forecasters with 5-day forecasts where the averaged 500-mb height anomaly correlation coefficients for the Northern Hemisphere were greater than 0.6. Observational data available to the GFS are much less than those at other numerical weather prediction centers, especially in the Tropics and Southern Hemisphere. The GRID messages of 5° resolution, ECMWF 24-h forecast 500-mb height and 850- and 200-mb wind fields available once a day on the Global Telecommunications System are used as supplemental observations to increase the data coverage for the GFS data assimilation. The supplemental data improve the GFS performance both in the analysis and forecast.

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