Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations

Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to six hours, which is a relevant lead time for emergency services in crisis time. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occured in southeastern France. Precipitation forecasts are evaluated at a 15 min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrologic point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15/1 h 30 of forecast, the performance of PIAF is higher than AROME-NWC. For longer lead times (up to 3 h) their performance are equivalent in general. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash-flood simulations, forced by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash-flood forecasting in terms of peak intensity, timing, and first rise of discharge, with an anticipation of these phenomena that can reach several hours.

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Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽

10.3390/atmos10100587 ◽

2019 ◽

Vol 10 (10) ◽

pp. 587

Author(s):

Magnus Lindskog ◽

Tomas Landelius

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Short Range ◽

Weather Prediction ◽

Heavy Precipitation ◽

Numerical Weather ◽

Extreme Precipitation Events ◽

Surface Data ◽

Enhanced Surface ◽

Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

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Forecasting length-of-day using numerical weather prediction models

Symposium - International Astronomical Union ◽

10.1017/s0074180900119618 ◽

1988 ◽

Vol 128 ◽

pp. 285-286

Author(s):

R. D. Rosen ◽

D. A. Salstein ◽

T. Nehrkorn ◽

J. O. Dickey ◽

T. M. Eubanks ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Prediction Models ◽

Weather Prediction ◽

Lead Times ◽

Length Of Day ◽

Wind Fields ◽

Numerical Weather ◽

Medium Range Forecast ◽

Frequency Changes ◽

Numerical Weather Prediction Models

A new approach to forecasting changes in length-of-day (δl.o.d) with lead times from one to ten days is examined. The approach is based on the high correlation that has been shown to exist between high frequency changes in l.o.d. and those in the atmosphere's angular momentum (M). Because forecasts of tropospheric values of M can be calculated from the zonal wind fields produced by operational numerical weather prediction models, it seems worth investigating whether these forecasts are sufficiently skillful to use to infer the evolution of δl.o.d. Here, we examine the quality of M forecasts made by the Medium Range Forecast (MRF) model of the U.S. National Meteorological Center (NMC). By comparing these forecasts against those based on a simple model of persistence, we find that skillful forecasts of M are being achieved on average by the MRF, although there has been much month-to-month variability in forecast quality. Overall, our results indicate that for prediction lead times of 1–10 days, dynamically-based forecasts of δl.o.d. represent a viable alternative to the empirical approaches currently in use.

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Dynamical contribution of mean potential vorticity pseudo-observations derived from MetOp/GOME2 ozone data into short-range weather forecast during high precipitation events

International Journal of Basic and Applied Sciences ◽

10.14419/ijbas.v4i2.4435 ◽

2015 ◽

Vol 4 (2) ◽

pp. 206

Author(s):

Siham Sbii ◽

Mimoun Zazoui ◽

Noureddine Semane

Keyword(s):

Potential Vorticity ◽

Short Range ◽

Initial Conditions ◽

Weather Prediction ◽

Dynamical Behavior ◽

Heavy Precipitation ◽

Limited Area ◽

Total Column Ozone ◽

Ozone Data ◽

Precipitation Events

Satellites are uniquely capable of providing uniform data coverage globally. Motivated by such capability, this study builds on a previously described methodology that generates numerical weather prediction initial conditions from satellite total column ozone data. The methodology is based on two principal steps. Firstly, the studied linear regression between vertical (100hPa-500hPa) Mean Potential Vorticity (MPV) and MetOp/GOME2 total ozone data (O3) generates MPV pseudo-observations. Secondly, the 3D variational (3D-Var) assimilation method is designed to take into account MPV pseudo-observations in addition to conventional observations.After a successful assimilation of MPV pseudo-observations using a 3D-Var approach within the Moroccan version of the ALADIN limited-area model, the present study aims to assess the dynamical behavior of the short-range forecast at upper levels during heavy precipitation events (HPEs). It is found that MPV assimilation offers the possibility to internally monitor the model upper-level dynamics in addition to the use of Water Vapor Satellite images.

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What Is the Predictability Limit of Midlatitude Weather?

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-18-0269.1 ◽

2019 ◽

Vol 76 (4) ◽

pp. 1077-1091 ◽

Cited By ~ 40

Author(s):

Fuqing Zhang ◽

Y. Qiang Sun ◽

Linus Magnusson ◽

Roberto Buizza ◽

Shian-Jiann Lin ◽

...

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Forecast Model ◽

Small Scale ◽

Lead Times ◽

Winter Storms ◽

Weather Forecasts ◽

Numerical Weather ◽

Forecast Lead Time ◽

Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

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A Seasonally Coherent Calibration (SCC) Model for Postprocessing Numerical Weather Predictions

Monthly Weather Review ◽

10.1175/mwr-d-19-0108.1 ◽

2019 ◽

Vol 147 (10) ◽

pp. 3633-3647 ◽

Cited By ~ 6

Author(s):

Q. J. Wang ◽

Tony Zhao ◽

Qichun Yang ◽

David Robertson

Keyword(s):

Weather Prediction ◽

Model Performance ◽

Statistical Characteristics ◽

Lead Times ◽

Model Parameters ◽

Calibration Model ◽

Numerical Weather ◽

Calibration Models ◽

Short Period ◽

One Year

Abstract Statistical calibration of forecasts from numerical weather prediction (NWP) models aims to produce forecasts that are unbiased, reliable in ensemble spread, and as skillful as possible. We suggest that the calibrated forecasts should also be coherent in climatology, including seasonality, consistent with observations. This is especially important when forecasts approach climatology as forecast skill becomes low, such as at long lead times. However, it is challenging to achieve these aims when data available to establish sophisticated calibration models are limited. Many NWP models have only a short period of archived data, typically one year or less, when they become officially operational. In this paper, we introduce a seasonally coherent calibration (SCC) model for working effectively with limited archived NWP data. Detailed rationale and mathematical formulations are presented. In the development of the model, three issues are resolved. These are 1) constructing a calibration model that is sophisticated enough to allow for seasonal variation in the statistical characteristics of raw forecasts and observations, 2) bringing climatology that is representative of long-term statistics into the calibration model, and 3) reducing the number of model parameters through sensible reparameterization to make the model workable with short NWP dataset. A case study is conducted to examine model assumptions and evaluate model performance. We find that the model assumptions are sound, and the developed SCC model produces well-calibrated forecasts.

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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

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;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.

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Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

Infrastructures ◽

10.3390/infrastructures5020015 ◽

2020 ◽

Vol 5 (2) ◽

pp. 15 ◽

Cited By ~ 2

Author(s):

Javier López Gómez ◽

Francisco Troncoso Pastoriza ◽

Enrique Granada Álvarez ◽

Pablo Eguía Oller

Keyword(s):

Local Level ◽

Weather Prediction ◽

Ground Truth ◽

Weather Conditions ◽

Meteorological Conditions ◽

Accurate Information ◽

Numerical Weather ◽

Weather Model ◽

Geostatistical Interpolation ◽

Solid Foundation

Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

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Evaluation of Numerical Weather Model–Based Satellite Precipitation Adjustment in Tropical Mountainous Regions

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0008.1 ◽

2019 ◽

Vol 20 (3) ◽

pp. 431-445 ◽

Cited By ~ 5

Author(s):

Xinxuan Zhang ◽

Emmanouil N. Anagnostou

Keyword(s):

Real Time ◽

Heavy Precipitation ◽

Precipitation Event ◽

Satellite Precipitation ◽

Numerical Weather ◽

Mountainous Regions ◽

Numerical Weather Model ◽

Weather Model ◽

Precipitation Estimates ◽

Daily Scale

Abstract The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-time and gauge-adjusted satellite products as well as the WRF simulation were evaluated by independent gauge networks at daily scale and event total scale. Results show that the near-real-time precipitation products exhibited severe underestimation relative to the gauge observations over the three tropical mountainous regions. The underestimation tended to be larger for higher rainfall accumulations. The WRF-based satellite adjustment provided considerable improvements to the near-real-time CMORPH and GSMaP products. Moreover, error metrics show that WRF-adjusted satellite products outperformed the gauge-adjusted counterparts for most of the events. The effectiveness of WRF-based satellite adjustment varied with events of different physical processes. Thus, the technique applied on satellite precipitation estimates of these events may exhibit inconsistencies in the bias correction.

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Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3587-2013 ◽

2013 ◽

Vol 17 (9) ◽

pp. 3587-3603 ◽

Cited By ~ 56

Author(s):

D. E. Robertson ◽

D. L. Shrestha ◽

Q. J. Wang

Keyword(s):

Numerical Weather Prediction ◽

Prediction Models ◽

Probability Distributions ◽

Weather Prediction ◽

Lead Times ◽

Streamflow Forecasting ◽

Post Processing ◽

Short Term ◽

Ensemble Forecasts ◽

Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

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HESS Opinions "On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?"

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2391-2011 ◽

2011 ◽

Vol 15 (7) ◽

pp. 2391-2400 ◽

Cited By ~ 14

Author(s):

F. Pappenberger ◽

H. L. Cloke ◽

A. Persson ◽

D. Demeritt

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Turning Points ◽

Flood Forecasting ◽

Lead Times ◽

Decision Framework ◽

Numerical Weather ◽

Undesirable Property

Abstract. Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments.

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