scholarly journals Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

2012 ◽  
Vol 121 (2) ◽  
pp. 273-285 ◽  
Author(s):  
S K ROY BHOWMIK ◽  
V R DURAI
Keyword(s):  
District Level ◽  
Indian Region ◽  
Multimodel Ensemble ◽  
Forecast System ◽  
Rainfall Forecast ◽  
Medium Range ◽  
Level Medium

Medium Range Atmospheric Forecast System: Evaluation Plan.

1985 ◽  
Author(s):  
P. A. Petit ◽  
H. D. Hamilton ◽  
R. L. Elsberry
Keyword(s):  
System Evaluation ◽  
Forecast System ◽  
Evaluation Plan ◽  
Medium Range

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.

scholarly journals Value addition in district level dynamical forecast during monsoon depressions and storms

MAUSAM ◽  
2021 ◽  
Vol 58 (1) ◽  
pp. 1-8
Author(s):  
O. P. SINGH ◽  
B. LAL ◽  
ONKARI PRASAD
Keyword(s):  
Sea Level ◽  
Heavy Rainfall ◽  
Rainfall Amount ◽  
District Level ◽  
Value Addition ◽  
Maximum Rainfall ◽  
Mean Sea Level ◽  
Weather Forecasts ◽  
Medium Range ◽  
Monsoon Depressions

ABSTRACT. The trials of district level forecasts yielded encouraging results during 2005 monsoon. The purpose of this paper is to document the methodology followed in the value addition during the periods of monsoon depressions and storms. The focus is on the use of Mean Sea Level (MSL) positions and the 850 hPa circulation features predicted by different model centres, especially the European Centre for Medium-Range Weather Forecasts (ECMWF). The ECMWF-predicted 72 hr MSL position of the monsoon depression centre was found to be significantly correlated to the actual position of the system and the central location of the realized rainfall zone associated with the system. Even the predicted location of the system at 850 hPa by the ECMWF has been found useful in identifying the districts that received heaviest rainfall associated with the monsoon systems.MM5 and T-80 – predicted locations of the system at 850 hPa yielded lower correlations with the location of the actual rainfall zone associated with the system. As ECMWF – predicted rainfall was not available the rainfall predicted by MM5 and T-80 were used in the computations of the correlations with actual rainfall amounts associated with monsoon depressions and storms. The correlations between MM5 and T-80 – predicted average and maximum rainfall associated with systems and corresponding actual were poor. Though it is not difficult to identify the districts that are likely to be affected by the heavy rainfall associated with monsoon depressions/storms, the prediction of exact rainfall amount for each district (beyond heavy, very heavy or exceptionally heavy categories) is difficult from the model outputs which makes such forecasts a very challenging task. Therefore, the value addition using other inputs such as satellite information, synoptic charts, climatology etc. are very useful in the prediction of rainfall amounts associated with monsoon systems.

scholarly journals Performance of a very high-resolution global forecast system model (GFS T1534) at 12.5 km over the Indian region during the 2016–2017 monsoon seasons

2019 ◽  
Vol 128 (6) ◽  
Author(s):  
P Mukhopadhyay ◽  
V S Prasad ◽  
R Phani Murali Krishna ◽  
Medha Deshpande ◽  
Malay Ganai ◽  
...  
Keyword(s):  
High Resolution ◽  
Indian Region ◽  
System Model ◽  
Global Forecast System ◽  
Forecast System ◽  
Very High

scholarly journals Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

2019 ◽  
Vol 147 (8) ◽  
pp. 2997-3023 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Ensemble Forecast ◽  
The Other ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Variable Resolution ◽  
Cell Spacing ◽  
Medium Range ◽  
Potential Benefits ◽  
Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

scholarly journals Utility of Global Ensemble Forecast System (GEFS) Reforecast for Medium-Range Drought Prediction in India

2016 ◽  
Vol 17 (6) ◽  
pp. 1781-1800 ◽  
Author(s):  
Reepal D. Shah ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Monsoon Season ◽  
Ensemble Forecast ◽  
Extreme Weather Events ◽  
Forecast System ◽  
Drought Prediction ◽  
Air Temperatures ◽  
Medium Range ◽  
Precipitation And Temperature

Abstract Medium-range (~7 days) forecasts of agricultural and hydrologic droughts can help in decision-making in agriculture and water resources management. India has witnessed severe losses due to extreme weather events during recent years and medium-range forecasts of precipitation, air temperatures (maximum and minimum), and hydrologic variables (root-zone soil moisture and runoff) can be valuable. Here, the skill of the Global Ensemble Forecast System (GEFS) reforecast of precipitation and air temperatures is evaluated using retrospective data for the period of 1985–2010. It is found that the GEFS forecast shows better skill in the nonmonsoon season than in the monsoon season in India. Moreover, skill in temperature forecast is higher than that of precipitation in both the monsoon and nonmonsoon seasons. The lower skill in forecasting precipitation during the monsoon season can be attributed to representation of intraseasonal variability in precipitation from the GEFS. Among the selected regions, the northern, northeastern, and core monsoon region showed relatively lower skill in the GEFS forecast. Temperature and precipitation forecasts were corrected from the GEFS using quantile–quantile (Q–Q) mapping and linear scaling, respectively. Bias-corrected forecasts for precipitation and air temperatures were improved over the raw forecasts. The influence of corrected and raw forcings on medium-range soil moisture, drought, and runoff forecasts was evaluated. The results showed that because of high persistence, medium-range soil moisture forecasts are largely determined by the initial hydrologic conditions. Bias correction of precipitation and temperature forecasts does not lead to significant improvement in the medium-range hydrologic forecasting of soil moisture and drought. However, bias correcting raw GEFS forecasts can provide better predictions of the forecasts of precipitation and temperature anomalies over India.

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