Improving QPF by blending techniques at the Meteorological Service of Catalonia

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

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Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

MAUSAM ◽

10.54302/mausam.v67i2.1298 ◽

2021 ◽

Vol 67 (2) ◽

pp. 323-332

Author(s):

ASHOK KUMAR DAS ◽

SURINDER KAUR

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

India Meteorological Department ◽

Precipitation Forecast ◽

False Alarms ◽

Model Ensemble ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Arw Model ◽

Nwp Model

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100, >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

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Evaluation and Correction of Quantitative Precipitation Forecast by Storm-Scale NWP Model in Jiangsu, China

Advances in Meteorology ◽

10.1155/2016/8476720 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Gaili Wang ◽

Dan Wang ◽

Ji Yang ◽

Liping Liu

Keyword(s):

Lead Time ◽

Weather Prediction ◽

Primary Objective ◽

Precipitation Forecast ◽

Lead Times ◽

Structure Amplitude ◽

Verification Method ◽

Amplitude Correction ◽

Regional Prediction ◽

Nwp Model

With the development of high-performance computer systems and data assimilation techniques, storm-scale numerical weather prediction (NWP) models are gradually used for short-term deterministic forecasts. The primary objective of this study is to evaluate and correct precipitation forecasts of a storm-scale NWP model called the advanced regional prediction system (ARPS). The evaluation and correction consider five heavy precipitation events that occurred in the summer of 2015 in Jiangsu, China. The performances of the original and corrected ARPS precipitation forecasts are evaluated as a function of lead time using standard measurements and a spatial verification method called Structure-Amplitude-Location (SAL). In general, the ARPS could not produce optimal forecasts for very short lead times, and the forecast accuracy improves with increasing lead time. The ARPS overestimates precipitation for all lead times, which is confirmed by large bias in many forecasts in the first and second quadrant of the diagram of SAL, especially at the 1 h lead time. The amplitude correction is performed by matching percentile values of the ARPS precipitation forecasts and observations for each lead time. Amplitude correction significantly improved the ARPS precipitation forecasts in terms of the considered performance indices of standard measures and A-component and S-component of SAL.

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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The Effect of Upstream Convection on Downstream Precipitation

Weather and Forecasting ◽

10.1175/waf986.1 ◽

2007 ◽

Vol 22 (2) ◽

pp. 255-277 ◽

Cited By ~ 15

Author(s):

Kelly M. Mahoney ◽

Gary M. Lackmann

Keyword(s):

Moisture Transport ◽

Weather Prediction ◽

The United States ◽

Cold Season ◽

Precipitation Forecast ◽

Study Region ◽

Low Level ◽

Convective Systems ◽

Eta Model ◽

Nwp Model

Abstract Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed.

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Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

10.5194/ems2021-379 ◽

2021 ◽

Author(s):

Sven Ulbrich ◽

Christian Welzbacher ◽

Kobra Khosravianghadikolaei ◽

Michael Hoff ◽

Alberto de Lozar ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Radar Data ◽

Precipitation Forecast ◽

Additional Measure ◽

Microphysics Scheme ◽

Numerical Weather ◽

Complementary Approach ◽

Forecast Quality

<p>The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.</p><p>At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) <span>making use of a</span> continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the <span>nowcasting </span>on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.</p><p>The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.</p>

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

10.5194/egusphere-egu21-13673 ◽

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.&#160; 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.&#160;</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.&#160;</p> </div>

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Sensitivity experiments of a severe rainfall event in north-western Italy: 17 August 2006

Advances in Science and Research ◽

10.5194/asr-2-133-2008 ◽

2008 ◽

Vol 2 (1) ◽

pp. 133-138 ◽

Cited By ~ 3

Author(s):

M. Milelli ◽

E. Oberto ◽

A. Parodi

Keyword(s):

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Precipitation Forecast ◽

Small Scale ◽

Moist Convection ◽

Cosmo Model ◽

Quantitative Precipitation Forecast ◽

Scale Modelling ◽

North Western ◽

Priority Project

Abstract. This study is embedded into a wider project named "Tackle deficiencies in Quantitative Precipitation Forecast (QPF)'' in the framework of the COSMO (COnsortium for Small-scale MOdelling) community. In fact QPF is an important purpose of a numerical weather prediction model, for forecasters and customers. Unfortunately, precipitation is also a very difficult parameter to forecast quantitatively. This priority project aims at looking into the COSMO Model deficiencies concerning QPF by running different numerical simulations of various events not correctly predicted by the model. In particular, this work refers to a severe event (moist convection) happened in Piemonte region during summer 2006. On one side the results suggest that details in orography representation have a strong influence on accuracy of QPF. On the other side COSMO Model exhibits a poor sensitivity on changes in numerical and physical settings when measured in terms of QPF improvements. The conclusions, although not too general, give some hint towards the behaviour of the COSMO Model in a typical convective situation.

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AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Mathematics ◽

10.3390/math9141653 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1653

Author(s):

Gabriela Czibula ◽

Andrei Mihai ◽

Alexandra-Ioana Albu ◽

Istvan-Gergely Czibula ◽

Sorin Burcea ◽

...

Keyword(s):

Binary Classification ◽

Weather Radar ◽

Predictive Performance ◽

Weather Conditions ◽

Radar Data ◽

Probability Of Detection ◽

Radar Reflectivity ◽

Classification Model ◽

Precipitation Forecast ◽

Short Term

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

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Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Atmosphere ◽

10.3390/atmos11030300 ◽

2020 ◽

Vol 11 (3) ◽

pp. 300 ◽

Cited By ~ 1

Author(s):

Aida Jabbari ◽

Deg-Hyo Bae

Keyword(s):

Least Squares ◽

Real Time ◽

Bias Correction ◽

Lead Time ◽

Weather Prediction ◽

Total Least Squares ◽

Time Dependent ◽

Precipitation Forecast ◽

Post Processing ◽

The Real

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.

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