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 High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016

2018 ◽  
Vol 19 (8) ◽  
pp. 1289-1304 ◽  
Author(s):  
Bong-Chul Seo ◽  
Felipe Quintero ◽  
Witold F. Krajewski
Keyword(s):  
High Resolution ◽  
Lead Time ◽  
Performance Metrics ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Flood Prediction ◽  
Forecast Lead Time ◽  
Basin Scale ◽  
Hydrologic Prediction ◽  
Map Analysis

Abstract This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling–Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1–3-h lead times and starts increasing with 4–6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.

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 Evaluating CFSv2 Subseasonal Forecast Skill with an Emphasis on Tropical Convection

2017 ◽  
Vol 145 (9) ◽  
pp. 3795-3815 ◽  
Author(s):  
Nicholas J. Weber ◽  
Clifford F. Mass
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Tropical Convection ◽  
Forecast Skill ◽  
Lead Times ◽  
Forecast Errors ◽  
Predictive Skill ◽  
Forecast Lead Time ◽  
Temporal Averaging ◽  
Total Variability

This study examines the subseasonal predictive skill of CFSv2, focusing on the spatial and temporal distributions of error for large-scale atmospheric variables and the realism of simulated tropical convection. Errors in a 4-member CFSv2 ensemble forecast saturate at lead times of approximately 3 weeks for 500-hPa geopotential height and 5 weeks for 200-hPa velocity potential. Forecast errors exceed those of climatology at lead times beyond 2 weeks. Sea surface temperature, which evolves more slowly than atmospheric fields, maintains skill over climatology through the first month. Spatial patterns of error are robust across lead times and temporal averaging periods, increasing in amplitude as lead time increases and temporal averaging period decreases. Several significant biases were found in the CFSv2 reforecasts, such as too little convection over tropical land and excessive convection over the ocean. The realism of simulated tropical convection and associated teleconnections degrades with forecast lead time. Large-scale tropical convection in CFSv2 is more stationary than observed. Forecast MJOs propagate eastward too slowly and those initiated over the Indian Ocean have trouble traversing beyond the Maritime Continent. The total variability of simulated propagating convection is concentrated at lower frequencies compared to observed convection, and is more fully described by a red spectrum, indicating weak representation of convectively coupled waves. These flaws in simulated tropical convection, which could be tied to problems with convective parameterization and associated mean state biases, affect atmospheric teleconnections and may degrade extended global forecast skill.

scholarly journals Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model

2013 ◽  
Vol 14 (4) ◽  
pp. 1075-1097 ◽  
Author(s):  
Hernan A. Moreno ◽  
Enrique R. Vivoni ◽  
David J. Gochis
Keyword(s):  
Lead Time ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Colorado Front Range ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Forecast Errors ◽  
Front Range ◽  
Distributed Hydrologic Model ◽  
Forecast Lead Time

Abstract Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.

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.

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

scholarly journals Assessing the predictability of WRF model for heavy rain by cold air associated with the easterly wind at high-level patterns over mid-central Vietnam

2018 ◽  
Vol 34 (1S) ◽  
Author(s):  
Nguyen Tien Toan ◽  
Cong Thanh ◽  
Pham Thi Phuong ◽  
Vu Tuan Anh
Keyword(s):  
Lead Time ◽  
Wrf Model ◽  
Heavy Rain ◽  
Rainfall Threshold ◽  
Precipitation Forecast ◽  
Easterly Wind ◽  
Cold Air ◽  
Central Vietnam ◽  
Forecast Lead Time ◽  
High Level

Abstract: In this study, the authors assessed the possibility of predicting precipitation due to cold air associated with the easterly wind at the high level in the WRF model with 2 days lead time for the Mid-Central Vietnam region. The results show that in the 24-hour forecasts lead time should use medium rainfall threshold (16-50 mm/day) and heavy rain (50-100 mm/day) to referent for quantitative precipitation forecast and rainfall area; for 48-hour forecast lead time should choose moderate rainfall threshold. The threshold of over 100 mm, the results of all of forecast lead time is not good, almost unpredictable. The results of this study can help forecasters have more information for forecasting rain due to cold air associated easterly wind at the high level for the Mid-Central Vietnam region.

scholarly journals Modeling the Near‐Surface Diurnal Cycle of Sea Surface Temperature in the Mediterranean Sea

2019 ◽  
Vol 124 (1) ◽  
pp. 171-183 ◽  
Author(s):  
S. Pimentel ◽  
W.‐H. Tse ◽  
H. Xu ◽  
D. Denaxa ◽  
E. Jansen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Diurnal Cycle ◽  
Sea Surface ◽  
Near Surface ◽  
The Mediterranean

scholarly journals A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

2011 ◽  
Vol 12 (5) ◽  
pp. 713-728 ◽  
Author(s):  
Lan Cuo ◽  
Thomas C. Pagano ◽  
Q. J. Wang
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Research Field ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts ◽  
Active Research ◽  
Nwp Model

Abstract Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

scholarly journals Evaluation of MM5 High-Resolution Real-Time Forecasts over the Urban Area of Athens, Greece

2004 ◽  
Vol 43 (11) ◽  
pp. 1666-1678 ◽  
Author(s):  
V. Kotroni ◽  
K. Lagouvardos
Keyword(s):  
Surface Temperature ◽  
Heat Waves ◽  
Thunderstorm Activity ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Parameterization Scheme ◽  
Cold Bias ◽  
Near Surface ◽  
Convective Parameterization ◽  
Convective Parameterization Scheme

Abstract In this paper the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forecast skill over an area of complex terrain is evaluated. Namely, the model is verified over a period of 1 yr (2002) over the greater area of Athens, Greece, for its near-surface temperature and wind forecasts, at 8- and 2-km grid spacing, but also over a 15-day period for the summer thunderstorm activity forecasts. For the near-surface temperature a cold bias is evident. The model is, in general, unable to reproduce the summer heat waves observed in the area. The increase of the grid resolution, from 8 to 2 km, results in an improvement of the forecast skill. Postprocessing of the forecasts by applying a Kalman-filtering correction method was very effective for both the 8- and the 2-km forecasts. For the forecast skill of wind, the analysis showed that there is not any net increase of the errors with increasing forecast time for the 48-h forecast period, the mean absolute errors, in general, present the lowest values at noontime, and the increase in resolution, from 8 to 2 km, results in a slight decrease of these errors. The analysis of the model skill to accurately forecast summertime precipitation showed that the 2-km simulations, without activation of the convective parameterization scheme, were unable to reproduce the observed thunderstorm activity. Sensitivity tests for the same period with simulations in which the convective parameterization was not activated for both the 8- and the 2-km simulations were still inaccurate, while activation of the convective parameterization scheme at all grids (even at 2 km) considerably increased the precipitation forecast skill.

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