Evaluation of Mei-yu heavy-rainfall quantitative precipitation forecasts in Taiwan by a cloud-resolving model for three seasons of 2012–2014

In this study, the performance of quantitative precipitation forecasts (QPFs) by the Cloud-Resolving Storm Simulator (CReSS) in Taiwan, at a horizontal grid spacing of 2.5 km and a domain size of 1500×1200 km2, in the range of 1–3 d during three Mei-yu seasons (May–June) of 2012–2014 is evaluated using categorical statistics, with an emphasis on heavy-rainfall events (≥100 mm per 24 h). The categorical statistics are chosen because the main hazards are landslides and floods in Taiwan, so predicting heavy rainfall at the correct location is important. The overall threat scores (TSs) of QPFs for all events on day 1 (0–24 h) are 0.18, 0.15, and 0.09 at thresholds of 100, 250, and 500 mm, respectively, and indicate considerable improvements at increased resolution compared to past results and 5 km models (TS < 0.1 at 100 mm and TS ≤ 0.02 at 250 mm). Moreover, the TSs are shown to be higher and the model more skillful in predicting larger events, in agreement with earlier findings for typhoons. After classification based on observed rainfall, the TSs of day − 1 QPFs for the largest 4 % of events by CReSS at 100, 250, and 500 mm (per 24 h) are 0.34, 0.24, and 0.16, respectively, and can reach 0.15 at 250 mm on day 2 (24–48 h) and 130 mm on day 3 (48–72 h). The larger events also exhibit higher probability of detection and lower false alarm ratio than smaller ones almost without exception across all thresholds. With the convection and terrain better resolved, the strength of the model is found to lie mainly in the topographic rainfall in Taiwan rather than migratory events that are more difficult to predict. Our results highlight the crucial importance of cloud-resolving capability and the size of fine mesh for heavy-rainfall QPFs in Taiwan.

SEMI-QUANTITATIVE PRECIPITATION FORECASTS FOR RIVER PUN PUN BY SYNOPTIC ANALOGUE METHOD

An attempt has been made to issue semi-quantitative precipitation forecasts for river Pun Pun by synoptic analogue method. Based upon twelve years data (1982-93) the study reveals that it is possible to issue semi-quantitative precipitation forecasts with confidence. The severe floods in the river Pun Pun pose problems to Patna town due to blocking effect of Ganga.

Semi quantitative forecasts for Baghmati/Adhwara Group of rivers/Kamala Balan catchments by synoptic analogue technique

An attempt has been made to issue semi-quantitative precipitation forecasts for Baghmati/Adhwara Group of rivers/Kamala-Balan catchments based upon 22 years data (1982-2003). The study reveals that it is possible to issue semi-quantitative precipitation forecast with confidence. Local topography and its steep gradient on Indo-Nepal Border are main factors that give birth to severe floods during south west monsoon and pose problems to Darbhanga City.

Semi-quantitative precipitation forecasts for Kosi/Mahananda catchment by synoptic analogue method

An attempt has been made to issue semi-quantitative precipitation forecasts for Kosi/Mahananda catchment by synoptic analogue method. Based upon 22 years of data (1982 - 2003) the study reveals that it is possible to issue semi­-quantitative forecasts with confidence. Local topography of the catchments and its steep gradient from Bhim nagar to Chatra / Brahkshetra in Kosi and hills in Darjeeling are favourable regions where moist air masses of the Bay of Bengal and the Arabian Sea during South West Monsoon in lower troposphere converge and trough at 500 hPa especially diffluent in rear creates divergence and moist air mass is pulled up resulting in heavy / very heavy rainfall in sub montane districts of Bihar and Nepal Himalaya in addition to orographic effects. This gives birth to severe floods and makes the life of densely populated districts of Pumea / Katihar / Saharsa / Kisanganj / Madhepura miserable and badly affects the economy of the region.

Evaluating Quantitative Precipitation Forecasts Using the 2.5 km CReSS Model for Typhoons in Taiwan: An Update through the 2015 Season

In this study, 24 h quantitative precipitation forecasts (QPFs) by a cloud-resolving model (with a grid spacing of 2.5 km) on days 1–3 for 29 typhoons in six seasons of 2010–2015 in Taiwan were examined using categorical scores and rain gauge data. The study represents an update from a previous study for 2010–2012, in order to produce more stable and robust statistics toward the high thresholds (typically with fewer sample points), which is our main focus of interest. This is important to better understand the model’s ability to predict such high-impact typhoon rainfall events. The overall threat scores (TS, defined as the fraction among all verification points that are correctly predicted to reach a given threshold to all points that are either observed or predicted to reach that threshold, or both) were 0.28 and 0.18 on day 1 (0–24 h) QPFs, 0.25 and 0.16 on day 2 (24–48 h) QPFs, and 0.15 and 0.08 on day 3 (48–72 h) QPFs at 350 mm and 500 mm, respectively, showing improvements over 5 km models. Moreover, as found previously, a strong dependence of higher TSs for larger rainfall events also existed, and the corresponding TSs at 350 and 500 mm for the top 5% of events were 0.39 and 0.25 on day 1, 0.38 and 0.21 on day 2, and 0.25 and 0.12 on day 3. Thus, for the top typhoon rainfall events that have the highest potential for hazards, the model exhibits an even higher ability for QPFs based on categorical scores. Furthermore, it is shown that the model has little tendency to overpredict or underpredict rainfall for all groups of events with different rainfall magnitude across all thresholds, except for some tendency to under-forecast for the largest event group on day 3. Some issues associated with categorical statistics to be aware of are also demonstrated and discussed.

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts toward improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.