Application of an Improved Analog-Based Heavy Precipitation Forecast Model to the Yangtze—Huai River Valley and Its Performance in June–July 2020

Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

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Evaluation of Daily Precipitation from the ERA5 Global Reanalysis against GHCN Observations in the Northeastern United States

Climate ◽

10.3390/cli8120148 ◽

2020 ◽

Vol 8 (12) ◽

pp. 148

Author(s):

Caitlin C. Crossett ◽

Alan K. Betts ◽

Lesley-Ann L. Dupigny-Giroux ◽

Arne Bomblies

Keyword(s):

United States ◽

Daily Precipitation ◽

Atlantic Coast ◽

Heavy Precipitation ◽

Northeastern United States ◽

Primary Input ◽

Spatial Continuity ◽

June July ◽

Global Reanalysis

Precipitation is a primary input for hydrologic, agricultural, and engineering models, so making accurate estimates of it across the landscape is critically important. While the distribution of in-situ measurements of precipitation can lead to challenges in spatial interpolation, gridded precipitation information is designed to produce a full coverage product. In this study, we compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the northeastern United States. We find that both the distance from the Atlantic Coast and elevation difference between ERA5 estimates and GHCN observations affect precipitation relationships between the two datasets. ERA5 has less precipitation along the coast than GHCN observations but more precipitation inland. Elevation differences between ERA5 and GHCN observations are positively correlated with precipitation differences. Isolated GHCN stations on mountain peaks, with elevations well above the ERA5 model grid elevation, have much higher precipitation. Summer months (June, July, and August) have slightly less precipitation in ERA5 than GHCN observations, perhaps due to the ERA5 convective parameterization scheme. The heavy precipitation accumulation above the 90th, 95th, and 99th percentile thresholds are very similar for ERA5 and the GHCN. We find that daily precipitation in the ERA5 dataset is comparable to GHCN observations in the northeastern United States and its gridded spatial continuity has advantages over in-situ point precipitation measurements for regional modeling applications.

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Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan

10.5194/egusphere-egu2020-4161 ◽

2020 ◽

Author(s):

Jing-Shan Hong ◽

Wen-Jou Chen ◽

Ying-Jhen Chen ◽

Siou-Ying Jiang ◽

Chin-Tzu Fong

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Heavy Precipitation ◽

Precipitation Forecast ◽

Preliminary Evaluation ◽

Satellite Mission ◽

Background Error ◽

Weather Bureau ◽

Central Weather Bureau ◽

Precipitation Events

<p>The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N. &#160;</p><p>In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.</p>

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A New Forecast Model Based on the Analog Method for Persistent Extreme Precipitation

Weather and Forecasting ◽

10.1175/waf-d-15-0174.1 ◽

2016 ◽

Vol 31 (4) ◽

pp. 1325-1341 ◽

Cited By ~ 10

Author(s):

Baiquan Zhou ◽

Panmao Zhai

Keyword(s):

Extreme Precipitation ◽

Cuckoo Search ◽

Forecast Model ◽

Lead Times ◽

Direct Model ◽

Huai River ◽

Accurate Forecast ◽

Mean Square Errors ◽

Temporal Sequences ◽

Better Than

Abstract This study aims to establish an analog prediction model for forecasting daily persistent extreme precipitation (PEP) during a PEP event (PEPE) using the temporal sequences of predictors with different weights applied in the atmospheric spatial field. The predictors are atmospheric variables in areas where the key influential systems of a PEPE are active in the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset. By means of the cosine similarity measure and the cuckoo search technique, a forecast model was established and named the Key Influential Systems Based Analog Model (KISAM). Validations through threat scores (TSs) and root-mean-square errors for PEP during 17–25 June 2010 indicate that KISAM is able to identify the approaching PEP earlier and yield a more accurate forecast for the location and intensity of PEP than direct model output (DMO) at 3-day and longer lead times in the Yangtze–Huai River valley. For the independent PEPE case on 17–19 June 2010, KISAM is able to predict the PEPE about 8 days in advance. That is much earlier than with DMO. In addition, KISAM produces better intensity forecasts and predicts the extent of the PEPE better than DMO at the same lead time of 5 days. In terms of the forecast experiments during June 2010 and 2015, KISAM shows relatively stronger capacity than DMO in predicting the occurrence and intensity of extreme precipitation (EP) and PEP events at lead times of 1 week or even longer. Through validation of more EP, better performance of KISAM compared to DMO on average is further confirmed at 3-day and longer lead times.

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A Regional Model Simulation of the 1991 Severe Precipitation Event over the Yangtze–Huai River Valley. Part II: Model Bias

Journal of Climate ◽

10.1175/1520-0442(2000)013<0093:armsot>2.0.co;2 ◽

2000 ◽

Vol 13 (1) ◽

pp. 93-108 ◽

Cited By ~ 14

Author(s):

Wei Gong ◽

Wei-Chyung Wang

Keyword(s):

Model Simulation ◽

Regional Model ◽

River Valley ◽

Precipitation Event ◽

Model Bias ◽

Huai River

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Association of the Rainy Season Precipitation with Low-Level Meridional Wind in the Yangtze River Valley and North China

Journal of Climate ◽

10.1175/jcli-d-10-05027.1 ◽

2012 ◽

Vol 25 (2) ◽

pp. 792-799 ◽

Cited By ~ 10

Author(s):

Gang Zeng ◽

Wei-Chyung Wang ◽

Caiming Shen

Keyword(s):

Rainy Season ◽

Yangtze River ◽

North China ◽

Precipitation Amount ◽

Meridional Wind ◽

Yangtze River Valley ◽

River Valley ◽

The Yangtze River ◽

Intergovernmental Panel ◽

June July

Abstract This study first used measurements to establish the association between the rainy season precipitation in the Yangtze River valley (YRV) and north China (NC) and the 850-hPa meridional wind, and then evaluated the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models’ simulations of both the associations and precipitation amount. It is shown that there exists a statistically significant positive correlation in the June–July precipitation and wind gradient over the YRV, and in the July–August precipitation and wind over NC. These associations are robust at daily, monthly, and interannual scales. Although many models are found to be capable of simulating the associations, the precipitation amount is still quite inadequate when compared with observations, thus raising the issue of the importance of lower-level wind simulations.

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Dependence of Predictability of Precipitation in the Northwestern Mediterranean Coastal Region on the Strength of Synoptic Control

10.5194/acp-2020-508 ◽

2020 ◽

Author(s):

Christian Keil ◽

Lucie Chabert ◽

Olivier Nuissier ◽

Laure Raynaud

Keyword(s):

Coastal Region ◽

Heavy Precipitation ◽

Forecast Model ◽

Precipitation Event ◽

Heavy Precipitation Event ◽

Precipitation Characteristics ◽

Synoptic Forcing ◽

Convective Adjustment ◽

Forecast Quality

Abstract. The weather regime dependent predictability of precipitation in the convection permitting kilometric scale AROME-EPS is examined for the entire HyMeX SOP1 employing the convective adjustment timescale. This diagnostic quantifies variations in synoptic forcing on precipitation and is associated with different precipitation characteristics, forecast skill and predictability. During strong synoptic control, which is dominating the weather on 80 % of the days in the 2-months period, the domain integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of most intense precipitation in the afternoon, as quantified with its 95th percentiles, is superior during weakly forced synoptic regimes. The study also considers a prominent heavy precipitation event that occurred during the NAWDEX field campaign in the same region, and the predictability during this event is compared with the events that occurred during HyMeX. It is shown that the unconditional evaluation of precipitation widely parallels the strongly forced weather type evaluation and obscures forecast model characteristics typical for weak control.

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