Rain Forecasts Research Articles

GPS Radio Occultation Data Assimilation in the AREM Regional Numerical Weather Prediction Model for Flood Forecasts

Based on the Backward Four-Dimensional Variational Data Assimilation (Backward-4DVar) system with the Advanced Regional Eta-coordinate Model (AREM), which is capable of assimilating radio occultation data, a heavy rainfall case study is performed using GPS radio occultation (GPS RO) data and routine GTS data on July 5, 2007. The case study results indicate that the use of radio occultation data after quality control can improve the quality of the analysis to be similar to that of the observations and, thus, have a positive effect when improving 24-hour rainfall forecasts. Batch tests for 119 days from May to August during the flood season in 2009 show that only the use of GPS RO data can make positive improvements in both 24-hour and 48-hour regional rainfall forecasts and obtain a better B score for 24-hour forecasts and better TS score for 48-hour forecasts. When using radio occultation refractivity data and conventional radiosonde data, the results indicate that radio occultation refractivity data can achieve a better performance for 48-hour forecasts of light rain and heavy rain.

Pronóstico de la ocurrencia de precipitación diaria en un gradiente altitudinal en el Sur del Ecuador mediante un generador climático

El pronóstico de precipitación es fundamental para mejorar la gestión de los recursos hídricos tanto para proyectos de aprovechamiento como de reducción de riesgos. Debido a su alta variabilidad, es un reto realizar un buen pronóstico de lluvia. En el presente estudio se empleó un generador climático (GC) para investigar la calidad del pronóstico de ocurrencia de precipitación diaria en una zona de montaña. El GC implementado usó como predictores a tres variables: la variable binaria ocurrencia del día anterior (Kt-1), las temperaturas máximas y mínimas del día anterior (Txt-1, Tnt-1, respectivamente); y dos covariables: las anomalías de la Temperatura mensual Superficial del Mar de las Regiones 1+2 y 3.4 de El Niño-Oscilación del Sur (Niño 1+2 y Niño 3.4 respectivamente). Determinamos que las variables Kt-1, Tnt-1 y la covariable Niño 3,4 mejoran el ajuste en el pronóstico. Encontramos diferencias notables en el número de días consecutivos secos y lluviosos en el gradiente altitudinal en un periodo lluvioso. El estudio permitió el planteamiento de varias hipótesis a partir del uso del GC las cuales ayudan a la comprensión del funcionamiento del sistema climático y el mejoramiento del pronóstico de precipitación.

Application of ensemble Kalman filter in WRF model to forecast rainfall on monsoon onset period in South Vietnam

Application of ensemble Kalman filter in WRF model to forecast rainfall on monsoon onset period in South Vietnam

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

GNSS atmospheric water vapor monitoring is not yet routinely performed in Italy, particularly at the regional scale. However, in order to support the activities of regional environmental protection agencies, there is a widespread need to improve forecasting of heavy rainfall events. Localized convective rain forecasts are often misplaced in space and/or time, causing inefficiencies in risk mitigation activities. Water vapor information can be used to improve these forecasts. In collaboration with the environmental protection agencies of the Lombardy and Piedmont regions in northern Italy, we have collected and processed GNSS and weather station datasets for two heavy rain events: one which was spatially widespread, and another which was limited to few square kilometers. The time variations in water vapor derived from a regional GNSS network with inter-station distances on the order of 50 km were analyzed, and the relationship between the time variations and the evolution of the rain events was evaluated. Results showed a signature associated with the passage of the widespread rain front over each GNSS station within the area of interest. There was a peak in the precipitable water vapor value when the heavier precipitation area surrounded the station, followed by a steep decrease (5–10 mm in about 1 h) as the rainclouds moved past the station. The smaller-scale event, a convective storm a few kilometers in extent, was not detected by the regional GNSS network, but strong fluctuations in water vapor were detected by a low-cost station located near the area of interest.

Crop Yield and Water Use Efficiency as Affected by Different Soil-Based Management Methods for Variable-Rate Irrigation in a Semi-Humid Climate

Abstract. To improve the management of variable-rate irrigation (VRI) systems in semi-humid climates, three different soil-based irrigation management methods were evaluated on their potential for reducing irrigation water use and maximizing crop yield and water use efficiency (WUE) during the 2016 and 2017 growing seasons of summer maize in the North China Plain. The three irrigation management methods evaluated were soil water balance modeling (SWB), measured soil water content (SWC), and a combination of SWB and the rain forecast for the next three days (RF). The experiments were implemented on four management zones delineated by available soil water holding capacity of a center-pivot VRI system. A similar irrigation trigger point (70% of field capacity) was used for the three irrigation management methods in the four management zones. In the two seasons, the total water application in the SWC treatments varied in a larger range among the management zones, and the irrigation water applied was 22% and 21% less than in the SWB and RF treatments, respectively. Similar yields were obtained among the irrigation management methods in both seasons. The maximum WUE was always observed with the SWC treatments for the four management zones in the 2017 season. The WUE with the SWC treatments was 36% and 23% higher than with the SWB and RF treatments, respectively. Considering the amount of irrigation water applied, yield, and WUE, our results demonstrated that the SWC method was more suitable for VRI management than the SWB and RF methods in this semi-humid climate. Keywords: Center pivot, Soil water balance, Soil water content, Rain forecast, Summer maize, Yield.

A Case Study of a Regional Torrential Rain Caused by Typhoon in North China

By using the NCEP/GFS analysis data, CIMISS data, JMA and China’s Typhoon Networks, heavy rainstorm occurred in east of North China associated with Typhoon Damrey from August 3rd to 4th, 2012 was analyzed. Results show during Damrey was going nearby Tianjin City and Hebei province of China, heavy rainstorm was observed in the cities of Qinhuangdao and Tangshan. The southerly jet stream from the southern side of the subtropical high and the periphery of Typhoon Saola is the conveyor belt for water vapor and energy, which enables Damrey to maintain for a long time and provide water vapor and heat conditions for rainfall in east of North China. The structure of Damrey caused a strong updraft in east of North China with a K-index greater than 35°C, which provided a favorable condition for the heavy rainstorm. The typhoon rainstorms in North China are the result of the interaction of the westerly, subtropical and tropical systems. In this heavy rain period, there was an obvious interaction between subtropical and tropical systems. This study has investigated the interaction between the northwestern Pacific typhoon and the North China heavy rainstorm, including the circulation characteristics of the typhoon and North China before and after the regional heavy rainstorm. Additionally, the climate background provides a reliable basis for the heavy rain forecast.

A method for implementing the real-time dynamic control of flood-limited water level

Many studies have proven that the dynamic control of flood-limited water level (DC-FLWL) is an effective methodology to balance flood control and water conservation for reservoir operations during the flood season. However, these studies have focused on determining the bound of FLWL; few studies have investigated the implementation of real-time DC-FLWL, which actually is the core of DC-FLWL. To implement real-time DC-FLWL, a new method for considering rain forecast is presented in the present study. The proposed method includes two procedures: prerelease and prefill, which constitute the prerelease–refill and the prefill–prerelease schemes. For each scheme, the categorical rain forecast with a lead time of 24 h is used as the trigger for different actions, while hydrological forecast and discharge are used to calculate the action details, including the water level reserved and the amount of discharge water. The method is examined via the operation of the flood event “19960810,” which occurred in the catchment of the Biliuhe reservoir in China. The results show that although the maximum water level and the maximum discharge of the three schemes have the same value, the water level at the end of both the prerelease–refill and the prefill–prerelease schemes of DC-FLWL is 68.8 and 68.5 m, which are both higher than the planning scheme, i.e., 68.1 m. These findings further confirm the effectiveness of DC-FLWL and also prove that the proposed method is suitable for implementing real-time DC-FLWL.

No one can serve two epistemic masters

Consider two epistemic experts—for concreteness, let them be two weather forecasters. Suppose that you aren’t certain that they will issue identical forecasts, and you would like to proportion your degrees of belief to theirs in the following way: first, conditional on either’s forecast of rain being x, you’d like your own degree of belief in rain to be x. Secondly, conditional on them issuing different forecasts of rain, you’d like your own degree of belief in rain to be some weighted average of the forecast of each (perhaps with weights determined by their prior reliability). Finally, you’d like your degrees of belief to be given by an orthodox probability measure. Moderate ambitions, all. But you can’t always get what you want.

An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan Plateau

Abstract. Using the National Oceanic and Atmospheric Administration's Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research's Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and showed an orographic bias described as an overestimation upwind of the mountains and an underestimation in the south of the rain belt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24 h and last 24 h of accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy-rainfall periods of 3–5, 13–16, and 22–25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3–5 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy-rainfall process than the control. Overall, based on the experiments in July 2015, the satellite data assimilation improved to some extent the prediction of the precipitation pattern over the Tibetan Plateau, although the simulation of the rain belt without data assimilation shows the regional shifting.

Rain and museum attendance: Are daily data fine enough?

I investigate the intraday effects of rain on the demand for indoor leisure. To do so, I use sub-daily museum attendance and weather data to reveal a dynamic response to precipitation that would be obscured using day-level data. I find that the magnitudes and signs of the effects of rainfall vary significantly throughout the day. In some hours, the predicted increase in visitors is nearly three times larger than would be expected from estimates using daily measures. Many individuals appear to actively adjust their plans throughout the day in response to rain, while others’ attendance depends upon prior weather forecasts of rain. Further analysis reveals that visit duration also increases during rainy periods, and visitors are more likely to attend pay-to-enter special exhibits. International visitors make up a greater share of total visitors during periods of observed precipitation. More broadly, this paper establishes the viability and value of working with widely available sub-daily rainfall data to uncover these dynamics.

一次高影响小雨过程预报失误的原因初探

2010年冬春之交的4月，因对8~9日发生在上海地区的一次小雨过程的预报失误，导致9日最高气温的预报与实况相差近7度，市民反响强烈。本文对这一“高影响”小雨过程的预报失误原因，进行了初步分析，结果表明：低层(850 hPa)的中尺度锋生，减缓了低层短波槽系统的过境速度，使短波槽系统在过境时出现“前倾”的异常垂直结构，导致高空(500 hPa)槽线虽已过境，但小雨过程却(因低层低值系统尚未过境)不止的令预报员始料未及的现象；预报员的惯性预报思维在前期预报已出现明显偏差的情况下未及时调整，对数值模式的中尺度预报信息分析不够深入，特别是模式对弱天气过程的要素预报能力把握不准等均是最终造成这次预报失误的主要原因。文章最后还给出了上海地区春季小雨过程预报及数值模式对弱天气过程诊断量改进的几点启示。 Due to the failure forecast on a light rain of 8-9 April 2010 in Shanghai, the highest temperature forecast is fault, the error near 7˚C. In this paper, the possible reasons of failure forecast on the high-impact light rain are discussed. The results show that: the lower (850 hPa) mesoscale fron-togenesis slows down the moving speed of low-level shortwave trough, causing the shortwave trough appearing a unusual “forward-lean” vertical structure during the trough movement, lead-ing to the trough line of high-level (500 hPa) passing through Shanghai but the lower-pressure system haven't and the light rain process sustains. Obviously, the unexpected phenomenon is difficult to forecast in operation. Furthermore, the forecast results have not been timely adjusted when the early forecasts have already shown the significant deviations, because of the inertia forecasting thinking. And the mesoscale analysis based on the numerical model products is not in-depth enough. Particularly, the limited capabilities of numerical model forecast on the weak process and its weather elements, are the main reasons for ultimate forecast failure. Finally, some inspirations on the forecast of spring light rain in Shanghai and model using skill on the weak weather process are shown in this paper.

Reply to “Comments on ‘Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence’”

AbstractMathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.

ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO

In this paper was used the kernel density estimation (KDE), a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic precipitation forecast, from an ensemble prediction with the WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables, for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

Value‐added quantitative medium‐range rainfall forecasts for the BIMSTEC region

ABSTRACT:Daily operational forecasts from the global models run at the National Centre for Medium Range Weather Forecasting (NCMRWF), India were assessed for their skill during the southwest monsoon for member countries of the Bay of Bengal Initiative for Multi‐Sectoral Technical and Economic Cooperation (BIMSTEC). The rainfall forecasts from two models, NCMRWF Global Forecast System (NGFS) and NCMRWF Unified Model (NCUM), were used to carry out the verification study for July and August 2013 and 2014. The skills were analysed in terms of equitable threat score (ETS), Hansen–Kuiper skill score (HKS) and the economic value for various rainfall thresholds. The ETS had a positive value for all of the thresholds up to the Day 10 forecast for both years. The HKS and economic value for binary forecast of Yes rain (prediction that rainfall event will occur) suggest that the model has significant positive skills. Deterministic forecast for four rainfall thresholds show negative skills, suggesting that false alarms increase as forecasts are made for smaller rainfall thresholds. To improve the skill scores, forecasted data were processed using simple bias correction, relative co‐efficient of variance (Rvar) method and the best easy systematic error (BESE) correction method. Correlation of the processed forecasts with model forecasts suggests an improvement using the bias correction and Rvar method but not using the BESE method. Significant improvement in the HKS and economic value of processed forecasts using the bias correction method has been noticed over most parts of the BIMSTEC region, except for the Western Ghats.

호우특보 평가 및 개선방안 연구

호우특보 평가 및 개선방안 연구

Scope and Prospects of Non-Invasive Visual Inspection Systems for Industrial Applications

This research has been done to facilitate the industries and researchers with a new concept and emerging trends in the field of non-destructive visual inspections. Visual inspection is a process of determining the degree of deviation from a given set of specifications. And, visual inspection system (VIS) is a method of data acquisition, data analysis, quality control, electrical system control, and process control for a particular product, system or process. The main processes involved in a visual inspection system are: image acquisition, image de-noising, image enhancement, image segmentation, image feature selection and extraction, image classification, feature matching, decision making, display of results, and generation of controlling signals according to set values and parameters. The major challenges were identified while the designing and implementation of VIS are: illumination intensity, illumination angle, camera angle, selection of resolution of camera, selection of type of camera, setting of image acquisition rate, development of segmentation algorithm, and selection of an appropriate image processing techniques for a particular system. According to survey it is found that off line VIS based analysis is mostly being applied to analyze the quality of product. Although, non-conventional methods have got higher popularity than conventional methods but non-conventional methods are generally developed for particular applications and it is not flexible enough to be used for other similar inspections, on the other hand, the conventional methods such as: statistical approach and neural network based approach are reliable, flexible, and generalizable. The main categories of VIS were identified as On Line, Off Line and Real Time Visual Inspection Systems. The main area of VIS applications are: space craft inspection, railway track inspection, road traffic density inspection, rain forecasting, real time monitoring and controlling of automotive vehicles, inspection of turbines, and patient action monitoring.

Neural networks-based operational prototype for flash flood forecasting: application to Liane flash floods (France)

The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogne-sur-Mer . Due to the complexity of land cover and hydrologic processes, a black-box non-linear modelling was chosen using neural networks. The multilayer perceptron model, known for its property of universal approximation is thus chosen. Four models were designed, each one for one forecasting horizon using rainfall forecasts: 24h, 12h, 6h, 3h. The desired output of the model is original: it represents the maximal value of the water level respectively 24h, 12h, 6h, 3h ahead. Working with best forecasts of rain (the observed ones during the event in the past), on the major flood of the database in test set, the model provides excellent forecasts. Nash criteria calculated for the four lead times are 0.98 (3h), 0.97 (6h), 0.91 (12h), 0.89 (24h). Designed models were thus estimated as efficient enough to be implemented in a specific tool devoted to real time operational use. The software tool is described hereafter: designed in Java, it presents a friendly interface allowing applying various scenarios of future rainfalls, and a graphical visualization of the predicted maximum water levels and their associated real time observed values.

Extended range (10–30 days) heavy rain forecasting study based on a nonlinear cross-prediction error model

Extended range (10–30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1–2 d, 3–9 d and 10–30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10–30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.

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The objective was the application of the WRF meteorological model and verify its rainfall forecast in the Cuba rainy season in 2014, for that two domains were built, one external of 24 x 24 km and one internal of 8 x 8 km resolution. Some of the used parameterization schemes were the ACM2 for boundary layer and the Kain-Fritsch for the convection. Rainfall forecasting was evaluated from 6 to 42 hours. The verification was performed using data from the Meteorological Station Network in Cuba. Two methods were used, one based on the punctual verification for the quantitative forecast, and another known as “partial verification” used for alternative forecast. As results, the WRF model implementation and its verification are achieved, which determined that this model underestimates the rainfall magnitudes, although deviations don’t overcome the 5 mm respect to the real one in the afternoons. From the occurrence viewpoint or not of the “rain” event the model also underestimates, but achieves high detection levels, 81 % for the forecast term 06-12 hours and 73 % for the term 30-36 hours. We conclude that the WRF model slightly underestimates the rainfall magnitude, but achieves high detection levels, which is really useful for making rain forecasts.

Accuracy of Rain Forecasts for Use in Scheduling Late Blight Management Tactics in the Columbia Basin of Washington and Oregon.

Accuracy of prediction was analyzed for 17- and 30-day rain forecasts at two locations in the Columbia Basin to determine whether forecasts were sufficiently accurate to be included as a model component to schedule fungicide applications for potato late blight. Accuracy was partitioned into specificity (percentage of forecasted nonrainfall events classified correctly) and sensitivity (percentage of forecasted daily rainfall events classified correctly). An adjusted sensitivity, which included the forecasted rain day plus the next 2 days, was also used to give a wider target than only 1 day for evaluating accuracy of forecasted rain events. For 17-day forecasts, specificity during the seasonal test period was ≥70% from mid-June through September and specificity over the days of the forecast was >70% for the first 8 days at both locations both years. Adjusted sensitivity over days of the forecast was initially >80% and then decreased as forecasts increased from 7 to 17 days for 17-day forecasts at both locations and years. Sensitivity and adjusted sensitivity during the seasonal test period were both positively correlated with the number of rainy days while specificity was negatively correlated. Adjusted sensitivity was considerably higher for May (month with highest incidence of rain) than July (month with lowest incidence of rain) at both locations. For 30-day forecasts, specificity during the test period was >75% in July and August and adjusted sensitivity ranged from 60 to 100% for time periods occurring in May and June during both sample seasons. Specificity was generally above 80% as days of the forecast increased and adjusted sensitivity varied greatly over days of the forecasts, with extremes between 0 and 100% at both locations and years for the 30-day forecasts. Specificity of 17- and 30-day rain forecasts and adjusted sensitivity of 17-day rain forecasts have utility in scheduling late blight fungicides in the Columbia Basin.