scholarly journals Some Characteristics of Model-Predicted Precipitation during the Summer Monsoon over India

2005 ◽  
Vol 44 (3) ◽  
pp. 324-339 ◽  
Author(s):  
B. K. Basu
Keyword(s):  
Summer Monsoon ◽  
Lead Time ◽  
Precipitation Pattern ◽  
Spatial Average ◽  
Spatial Averaging ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Ecmwf Model ◽  
Rainy Days ◽  
Range Forecasts

Abstract For the summer monsoon seasons of 1995, 1996, and 1997 the day-1 to day-4 forecasts of precipitation from both the National Centre for Medium Range Weather Forecasting (NCMRWF) and the European Centre for Medium-Range Forecasts (ECMWF) models reproduce the main features of the observed precipitation pattern when averaged over the whole season. On average, less than 30% of all rain gauge stations in India report rain on a given day during the monsoon season. The number of observed rainy days increases to 41% after spatial averaging over ECMWF model grid boxes and to 50% after spatial averaging over NCMRWF model grid boxes. The NCMRWF model forecasts have 10%–15% more rainy days, mostly in the light or moderate precipitation categories, when compared with the spatial average of observed values. Seasonal accumulated values of all of India’s average precipitation show a slight increase with the forecast lead time for the NCMRWF model and a small decrease for the ECMWF model. The weekly accumulated values of forecast precipitation from both models, averaged over the whole of India, are in good phase relationship (∼0.9 in most cases) with the observed value for forecasts with a lead time up to day 4. Values of statistical parameters, based on the frequency of occurrence in various classes, indicate that the NCMRWF model has some skill in predicting precipitation over India during the summer monsoon. The NCMRWF model forecasts have higher trend correlation with the observed precipitation over India than do the ECMWF model forecasts. The mean error in precipitation is, however, much less in the ECMWF model forecasts, and the spatial distribution of seasonal average medium-range forecasts of ECMWF is closer to that observed along the west coast mountain ridgeline.

scholarly journals Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10852
Author(s):  
Gregor Skok ◽  
Doruntina Hoxha ◽  
Žiga Zaplotnik
Keyword(s):  
Neural Networks ◽  
Lead Time ◽  
Forecast Error ◽  
Lead Times ◽  
Profile Measurement ◽  
Profile Data ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Range Forecasts

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.

scholarly journals The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

2010 ◽  
Vol 138 (10) ◽  
pp. 3787-3805 ◽  
Author(s):  
Arindam Chakraborty
Keyword(s):  
Surface Temperature ◽  
Diurnal Cycle ◽  
Lead Time ◽  
Tropical Convection ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Near Surface ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Ecmwf Model

Abstract This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4–5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air–sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with “zero” precipitation and heavy (>40 mm day−1) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2–4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

scholarly journals Dynamically vs. empirically downscaled medium-range precipitation forecasts

2009 ◽  
Vol 13 (9) ◽  
pp. 1649-1658 ◽  
Author(s):  
G. Bürger
Keyword(s):  
Dynamical System ◽  
Lead Time ◽  
Regional Model ◽  
The Other ◽  
Medium Range Forecast ◽  
Mountainous Catchments ◽  
Skill Scores ◽  
Medium Range ◽  
Ecmwf Model ◽  
Empirical System

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

2021 ◽  
Author(s):  
Ganesh R. Ghimire ◽  
Witold F. Krajewski ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Time Scale ◽  
Lead Time ◽  
Time Horizon ◽  
Streamflow Forecasting ◽  
Forecasting Accuracy ◽  
Trade Off ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Forecasting System

AbstractIncorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time (tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

scholarly journals Dynamically vs. empirically downscaled medium-range precipitation forecasts

2009 ◽  
Vol 6 (2) ◽  
pp. 3517-3542
Author(s):  
G. Bürger
Keyword(s):  
Dynamical System ◽  
Lead Time ◽  
Regional Model ◽  
The Other ◽  
Medium Range Forecast ◽  
Mountainous Catchments ◽  
Skill Scores ◽  
Medium Range ◽  
Ecmwf Model ◽  
Empirical System

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing

2021 ◽  
Author(s):  
Stella Jes Varghese ◽  
Kavirajan Rajendran ◽  
Sajani Surendran ◽  
Arindam Chakraborty
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Lead Time ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Initial Conditions ◽  
Summer Monsoon Rainfall ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Central Pacific

<p>Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.</p>

scholarly journals Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

2016 ◽  
Vol 31 (3) ◽  
pp. 1001-1017 ◽  
Author(s):  
Omar V. Müller ◽  
Miguel A. Lovino ◽  
Ernesto H. Berbery
Keyword(s):  
South America ◽  
Lead Time ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Southern South America ◽  
Climate Conditions ◽  
Forecast Lead Time ◽  
Highly Correlated ◽  
Precipitation Season

Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

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