Short-range Precipitation Forecasts Research Articles

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

Sensitivity of the Short-Range Precipitation Forecast in South China Region to Sea Surface Temperature Variations

Ocean variability plays an essential role in the climate system at different time scales through air–sea interactions. Recent studies have addressed the importance of the ocean mixed layer in cooling feedback to tropical cyclones (TCs). However, using constant sea surface temperature (SST) in short-range weather forecasts remains common, especially in high-resolution regional models. This study investigates the role of subsurface ocean mixing in the short-range forecast of non-TC extreme rainfall with the Weather Research and Forecast (WRF) model. In the simulations of 26 heavy rainfall cases, we found that using a one-dimensional mixed layer model leads to a 15% enhancement (reduction) of rainfall maximum in six (two) cases compared to using constant SST. When the initial depth of the mixed layer model is perturbed by the amount of daily variability, 13 cases exhibit larger than 15% increases or decreases. A detailed analysis of one case suggests that the radiative process dominates the overall response of SST. The warming and moistening of boundary layer air cause significant strengthening of updrafts in convection. Although the SST change in most cases due to varying mixed layer model setups is less than 0.5 K, convective motions in some cases are surprisingly sensitive to small changes.

An Improved Weighting Method of Time-Lag-Ensemble Averaging for Hourly Precipitation Forecasts and Its Application in a Typhoon-Induced Heavy Rainfall Event

Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.

The Influence of Tropical Forecast Errors on Higher Latitude Predictions

AbstractThe atmospheric response to variations in tropical latent heating extends well beyond its source region, and therefore it is thought that a reduction of tropical forecast errors should also benefit subsequent forecasts over the extratropics. This relationship is evaluated using a conditional skill analysis applied to subseasonal reforecasts from the National Centers for Environmental Prediction Coupled Forecast System and the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System. It is shown that there is enhanced or attenuated skill in Northern Hemisphere Weeks 2–4 forecasts when tropical short range precipitation forecasts are “good” or “poor,” respectively. This conditional skill is modulated by both El Ni o Southern Oscillation and the Madden and Julian Oscillation, particularly in the Integrated Forecast System. The results presented here indicate that midlatitude Weeks 2–4 predictive skill would benefit from improvements in Week 1 tropical performance, particularly for the National Centers for Environmental Prediction system.

Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

Application of Titan Software for Recognition, Monitoring, Instantaneous Analysis of Thunderstorm for Ho Chi Minh City

Abstract: This paper presents the results of applied research on the TITAN software for the identification, monitoring and Instantaneous analysis of thunderstorms over Ho Chi Minh City area. The Tstorms2Symprod algorithm in TITAN software is used to determine the thunderstorm cells and movement of the thunderstorm cells in the next hour, through the real-time reflectivity, based on smoothing of the TREC vectors. Integral along time using Lagrange diagrams. These integral diagrams within 60 minutes create 10 product apart about once every 6 minutes. The results of this software assessed with observation data at the station, the Cmax product (radar maximum reflectivity) and the Himawari 8 satellite image. Studies show that the TITAN model can capture the location, direction, and velocity, the scale of the thunderstorm cells. This important to the very short-range precipitation forecast for Ho Chi Minh City
 
 Keyword: TITAN, Tstorms2Symprod,thunderstorms

A GIS-based modeling of snow accumulation and melt processes in the Votkinsk reservoir basin

Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale atmospheric Weather Research and Forecasting (WRF) model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin (184,319 km2). The method is tested during three winter seasons (2012–2015). The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent (SWE) and snow-covered areas (SCA). The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data (ATBD-MOD 10) has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements (snow survey).

Impact of Assimilation of MADRAS Geophysical Parameters on Short Range WRF Model Forecasts

This research paper presents the assimilation of ocean surface precipitable water (TPW) and wind speed obtained from Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard Megha-Tropics satellite retrievals into the Weather Research and Forecasting Model (WRF) model to assess their impact on short-range precipitation forecasts. Two parallel experiments are performed daily with and without assimilation of the MADRAS geophysical parameters during the entire month of the July 2012. Initially, MADRAS retrieved TPW and wind speed are compared with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) retrieved geophysical parameters and European Centre for Medium-Range Weather Forecasts (ECMWF) global model analyses. This comparison shows a root mean square difference (RMSD) of ∼1.8 ms−1 and ∼0.3 gcm−2, and mean difference (BIAS) of ∼−1.0 ms−1 and ∼0.15 gcm−2 in wind speed and TPW, respectively. Results show that the assimilation of MADRAS retrieved geophysical parameters improved the TPW and wind speed by about 20% and 10% reduction in RMSD, respectively. Six-hourly WRF model forecasts are also improved with the assimilation of MADRAS retrievals. The Forecast Impact (FI) parameter shows larger than 10 mm improvement in 24 h rainfall forecasts over the Indian Ocean.

Constraining a 3DVAR Radar Data Assimilation System with Large-Scale Analysis to Improve Short-Range Precipitation Forecasts

AbstractIt is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors’ experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar data are available. As a result, the 3DVAR analysis with the constraint is more accurate when verified against an independent dataset. The performance of this new constraint on improving precipitation forecasts is tested using six convective cases and verified against radar-derived precipitation by employing four skill indices. All of the skill indices show improved forecasts when using the methodology presented in this paper.

A Case Study of Impact of FY-2C Satellite Data in Cloud Analysis to Improve Short-Range Precipitation Forecast

AbstractChinese FengYun-2C (FY-2C) satellite data were combined into the Local Analysis and Prediction System (LAPS) model to obtain three-dimensional cloud parameters and rain content. These parameters analyzed by LAPS were used to initialize the Global/Regional Assimilation and Prediction System model (GRAPES) in China to predict precipitation in a rainstorm case in the country. Three prediction experiments were conducted and were used to investigate the impacts of FY-2C satellite data on cloud analysis of LAPS and on short range precipitation forecasts. In the first experiment, the initial cloud fields was zero value. In the second, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS without combining the satellite data. In the third experiment, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS including satellite data. The results indicated that the FY-2C satellite data combination in LAPS can show more realistic...

Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan

Abstract Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system. Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

Precipitation and Temperature Forecast Performance at the Weather Prediction Center

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

A Case Study of Impact of FY-2C Satellite Data in Cloud Analysis to Improve Short-Range Precipitation Forecast

中国 FengYun-2C (FY-2C ) 卫星数据被合为本地分析和预言系统(臀部) 模型获得三维的云参数和雨内容。臀部分析的这些参数被用来在中国初始化全球 / 地区性的吸收和预言系统模型(葡萄) 在这个国家在一个暴风雨盒子中预言降水。三个预言实验被进行并且被用来在臀部的云分析上并且在短范围降水预报上调查 FY-2C 卫星数据的影响。在第一个实验，起始的云回答是零价值。在里面第二，起始的云领域是没有联合卫星数据，内容从臀部导出的云液体水，云冰，和雨。在第三个实验，起始的云地是内容包括卫星数据从臀部导出的云液体水，云冰，和雨。结果显示在臀部的 FY-2C 卫星数据联合能显示出更现实主义的云分布，和模型模拟因为当卫星数据和复杂的云分析没被使用时，在 16 h 的降水在那上有某些改进。

Calibrated Short-Range Ensemble Precipitation Forecasts Using Extended Logistic Regression with Interaction Terms

Abstract Extended logistic regression has been shown to be a method well suited to calibrating precipitation forecasts from medium-range ensemble prediction systems. The extension of the logistic regression unifies the separate predictive equations for each threshold, introducing the predictive threshold as part of the predictors. Mutually consistent probabilities and a reduction in the total number of regression parameters to be evaluated are part of the benefits of the extended approach. However, considering the predictive threshold as the only “unification” predictor constrains the regression parameters associated with the primary predictors to be constant with the threshold. To alleviate the rigidity of the extended scheme, interaction terms are introduced in the unified predictive equation. Within the framework of the convection-permitting German-focused Consortium for Small-Scale Modeling ensemble prediction system (COSMO-DE-EPS), it is shown that extended logistic regression, applied to short-range precipitation forecasts with the ensemble mean as the primary predictor, improves the performance of the system. Interaction effects are first illustrated through the analysis of regression parameters and then the positive impact on the calibrated forecasts of the new extended logistic regression scheme, including interaction terms, is shown using quantitative and qualitative measures of reliability and sharpness.

Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30 km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7 mm) event than light rainfall (for thresholds of 1 and 3 mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.

Experiments on the radar reflectivity data assimilation in the WRF-ARW model

Given are the results of numerical experiments on the radar reflectivity data assimilation in the nonhydrostatic WRF-ARW model. For this purpose, the procedure of three-dimensional variational data assimilation was used included as a separate block (WRF-3DVAR) in the WRF-ARW system. The experiments on the cyclic assimilation of radar information were carried out for ten days of summer of 2009. The data of six AKSOPRI complexes forming the Moscow ring of radar measurements were assimilated. The contribution of the radar reflectivity data assimilation to the quality of short-range precipitation forecasts was verified on the basis of the momentary and accumulated radar precipitation and total precipitation at the stations. It is obtained that the contribution of the radar reflectivity assimilation using the WRF-ARW algorithm to the precipitation forecast is close to neutral: the number of accurate forecasts increases by about 10%; however, the number of false alarms increases as well.

Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters’ subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July–24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

Optimized advection of radar reflectivities

A nowcasting system for generation of short-range precipitation forecasts has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). The methodology consists of utilising a time-series of radar reflectivity composites for deriving an advection field, which will give a better representation of the motion of the precipitation pattern compared to a model wind field. The advection field is derived applying a 4-dimensional variational data assimilation technique. The resulting field is then used for a semi-Lagrangian advection of the latest available reflectivity field forward in time. During the forecast, the advected field is gradually replaced by a numerical weather prediction forecast in order to include the onset of convection and advection into the radar coverage area. In an idealised example with simulated observations the functionality of the method is demonstrated. For a case study of a full scale example the resulting precipitation forecast shows large improvements compared to the operational numerical weather prediction model used at SMHI, especially for forecasts up to three hours, where the largest influence from the radar advection occurs. In an objective validation of the structure, amplitude and location of modelled precipitation, where the forecasts are compared to radar observations, these findings are confirmed. The same validation of model runs over a longer time period also clearly indicates that the amplitude, structure and location of the precipitation patterns are significantly improved as compared to a short-range forecast from the operational forecast model used at SMHI.

Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution

The aim of this paper is to determine whether the assimilation of extrapolated radar reflectivity can improve very short range precipitation forecasts in the summer season. The COSMO NWP model, with a horizontal resolution of 2.8 km, is applied, and radar reflectivity data are assimilated using a water vapour correction method. Two model runs are compared: the first run assimilates observed radar reflectivity, and the second run assimilates observed and extrapolated radar reflectivity. The extrapolation is performed by the COTREC method, and the length of extrapolation is 1 h. Hourly precipitation forecasts are compared and evaluated subjectively, both visually and by the Fractions Skill Score for three events with severe convective precipitation in the model domain. The results of the comparisons indicate that the assimilation of extrapolated radar reflectivity in most cases improves the accuracy of precipitation forecasts for the second and third lead hours. In general, hourly precipitation forecasts by the COSMO NWP model with the assimilation of radar reflectivity show reasonable skill for the first and the second hours.

GPS Zenith Total Delays and Precipitable Water in comparison with special meteorological observations in Verona (Italy)during MAP-SOP

Continuous meteorological examination of the Pre-Alpine zones in Northern Italy (Po Valley) is important for determination of atmospheric water cycles connected with floods and rainfalls. During a special meteorological observing period (MAP-SOP), radiosounding and other measurements were made in the site of Verona (Italy). This paper deals with Zenith Total Delay (ZTD) and Precipitable Water (PW) comparisons obtained by GPS, radiosounding and other meteorological measurements. PW and ZTD from ground-based GPS data in comparison with classical techniques (e.g., WVR, radiosounding) from recent literature present an accurate tool for use in meteorology applications (e.g., assimilation in Numerical Weather Prediction (NWP) models on short-range precipitation forecasts). Comparison of such ZTD for MAP-SOP showed a standard deviation of 16.1 mm and PW comparison showed a standard deviation of 2.7 mm, confirming the accuracy of GPS measurements for meteorology applications. In addition, PW data and its time variation are also matched with time series of meteorological situations. Those results indicate that changes in PW values could be connected to changes in air masses, i.e. to passages of both cold and warm fronts. There is also a correlation between precipitation, forthcoming increase and the following decrease of PW. A good agreement between oscillation of PW and precipitation and strong cyclonic activities is found.