Scale-Dependent Uncertainties in Global QPFs and QPEs from NWP Model and Satellite Fields

Abstract Global precipitation forecasts from numerical weather prediction (NWP) models can be verified using the near-global coverage of satellite precipitation retrievals. However, inaccuracies in satellite precipitation analyses complicate the interpretation of forecast errors that result from verification of an NWP model against satellite observations. In this study, assessments of both a global quantitative precipitation estimate (QPE) from a satellite precipitation product and corresponding global quantitative precipitation forecast (QPF) from a global NWP model are conducted using available global land-based gauge data. A scale decomposition technique is devised, coupled with seasonal and spatial classifications, to evaluate these inaccuracies. The results are then analyzed in context with various physical precipitation systems, including heavy monsoonal rains, light Mediterranean winter rains, and North American convective-related and midlatitude cyclone–related precipitation. In general, global model results tend to consistently overforecast rainfall, whereas satellite measurements present a mixed pattern, underestimating many large-scale precipitation systems while overestimating many convective-scale precipitation systems. Both global model QPF and satellite-retrieved QPE showed better correlation scores in large-scale precipitation systems when verified with gauge measurements. In this case, model-based QPF tends to outperform satellite-retrieved QPE. At convective scales, there are significant drops in both model QPF and satellite QPE correlation scores, but satellite QPE performs slightly better than model QPF. These general results also showed regional and seasonal variation. For example, in tropical monsoon systems, satellite QPE tended to outperform model-based QPF at both scales. Overall, the results suggest potential improvements for both satellite estimates and weather forecast systems, in particular as applied to global precipitation forecasts.

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Detection and thermal description of medicanes from numerical simulation

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1059-2014 ◽

2014 ◽

Vol 14 (5) ◽

pp. 1059-1070 ◽

Cited By ~ 19

Author(s):

M. A. Picornell ◽

J. Campins ◽

A. Jansà

Keyword(s):

Numerical Simulation ◽

Thermal Structure ◽

Weather Prediction ◽

Weather Forecast ◽

Small Scale ◽

Medium Range Weather Forecast ◽

Warm Core ◽

Strong Winds ◽

Nwp Model ◽

Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

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Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽

10.3390/atmos9120484 ◽

2018 ◽

Vol 9 (12) ◽

pp. 484 ◽

Cited By ~ 2

Author(s):

Ana Firanj Sremac ◽

Branislava Lalić ◽

Milena Marčić ◽

Ljiljana Dekić

Keyword(s):

Downy Mildew ◽

Fire Blight ◽

Meteorological Data ◽

Weather Prediction ◽

Weather Forecast ◽

Atmospheric Model ◽

Plasmopara Viticola ◽

Forecasting System ◽

Nwp Model ◽

The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

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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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Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

Applied Sciences ◽

10.3390/app10165493 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5493 ◽

Cited By ~ 1

Author(s):

Jingnan Wang ◽

Lifeng Zhang ◽

Jiping Guan ◽

Mingyang Zhang

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Weather Forecast ◽

Radar Data ◽

Precipitation Forecast ◽

Rainfall Forecast ◽

Skill Scores ◽

The Individual ◽

The Impact ◽

Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

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Towards operational postprocessing of cloud cover at MeteoSwiss

10.5194/egusphere-egu2020-17886 ◽

2020 ◽

Author(s):

Stephan Hemri ◽

Christoph Spirig ◽

Jonas Bhend ◽

Lionel Moret ◽

Mark Liniger

Keyword(s):

Cloud Cover ◽

Prediction Models ◽

Weather Prediction ◽

Weather Forecast ◽

Forecast Errors ◽

Observation Data ◽

Model Output ◽

Ensemble Forecasts ◽

Direct Model ◽

Ensemble Model Output Statistics

Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.

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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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Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

Advances in Meteorology ◽

10.1155/2016/1384302 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 9

Author(s):

Wansik Yu ◽

Eiichi Nakakita ◽

Sunmin Kim ◽

Kosei Yamaguchi

Keyword(s):

Initial Conditions ◽

Forecast Error ◽

Weather Prediction ◽

Uncertainty Propagation ◽

Precipitation Forecast ◽

Flood Forecast ◽

Catchment Scale ◽

Forecast Uncertainty ◽

Specific Discharge ◽

Nwp Model

The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP) model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale through distributed rainfall-runoff modeling based on the forecasted ensemble rainfall of NWP model. At first, forecast rainfall error based on the BIAS is compared with flood forecast error to assess the error propagation. Second, the variability of flood forecast uncertainty according to catchment scale is discussed using ensemble spread. Then we also assess the flood forecast uncertainty with catchment scale using an estimation regression equation between ensemble rainfall BIAS and discharge BIAS. Finally, the flood forecast uncertainty with RMSE using specific discharge in catchment scale is discussed. Our study is carried out and verified using the largest flood event by typhoon “Talas” of 2011 over the 33 subcatchments of Shingu river basin (2,360 km2), which is located in the Kii Peninsula, Japan.

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Improving QPF by blending techniques at the Meteorological Service of Catalonia

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-1443-2010 ◽

2010 ◽

Vol 10 (7) ◽

pp. 1443-1455 ◽

Cited By ~ 28

Author(s):

A. Atencia ◽

T. Rigo ◽

A. Sairouni ◽

J. Moré ◽

J. Bech ◽

...

Keyword(s):

Lead Time ◽

Weather Prediction ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Short Term ◽

Convective Structures ◽

Prediction Horizon ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Nwp Model

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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Application to NWP Models Verification of an Atmospheric Circulation Patterns Classification

10.5194/ems2021-36 ◽

2021 ◽

Author(s):

Raffaele Salerno ◽

Laura Bertolani

Keyword(s):

Large Scale ◽

Precipitation Forecast ◽

Specific Humidity ◽

Forecast Errors ◽

Self Organizing Map ◽

Model Ensemble ◽

Climate Services ◽

Global Models ◽

Circulation Patterns ◽

Synoptic Circulation

At Meteo Expert, a Italian private organization providing weather and climate services and formerly known as Epson Meteo Centre, we are using the Self Organizing Map (SOM) algorithm to study synoptic circulation over Southern Europe, evaluating the capability of five NWP global models and one multi-model ensemble to predict its variability in order to relate synoptic circulation patterns to temperature and precipitation forecast&#8217;s quality over Italy. SOM is an iterative algorithm that &#8216;learns&#8217; the patterns of the input data vectors and organizes them into nodes within the SOM space, arranging like patterns in neighboring nodes and the most unlike patterns in nodes farthest from each other. Daily observed and predicted weather types from the five NWP global models and the multi-model ensemble were recognized and classified by the SOM. The SOM-based classification built for our purposes produces a 12-weather-type set using daily 500 hPa and 700 hPa geopotential, sea level pressure, 850 hPa temperature and 700 hPa specific humidity. The five global models are GFS from National Centers for Environmental Prediction, IFS from European Centre for Medium-Range Weather (ECMWF), Arpege from Meteo France, GEM from Canadian Meteorological Centre, ICON from Deutscher Wetterdienst, together with MIX, our multi-model ensemble. Here we would like to present some examples of this operational activity in the one-year-period, also showing how much the source of forecast errors may depend on large-scale dynamics rather than model's physical parameterisations. A quality index has been used to quantify the overall ability of models in predicting the circulation patterns, showing that MIX and ECMWF reached the best performance within 96 hours of forecast.

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Potential vorticity dynamics of life cycles of blocked weather regimes in the Euro-Atlantic region: A diagnostic framework

10.5194/egusphere-egu21-12847 ◽

2021 ◽

Author(s):

Seraphine Hauser ◽

Christian M. Grams ◽

Michael Riemer ◽

Peter Knippertz ◽

Franziska Teubler

Keyword(s):

Potential Vorticity ◽

Large Scale ◽

Climate Models ◽

Numerical Models ◽

Weather Prediction ◽

Life Cycles ◽

Forecast Errors ◽

Atlantic Region ◽

Weather Regimes ◽

Medium Range

Quasi-stationary, persistent, and recurrent states of the large-scale extratropical circulation, so-called weather regimes, characterize the atmospheric variability on sub-seasonal timescales of several days to a few weeks. Weather regimes featuring a blocking anticyclone are of particular interest due to their long lifetime and potential for high-impact weather. However, state-of-the-art numerical weather prediction and climate models struggle to correctly represent blocking life cycles, which results in large forecast errors at the medium-range to sub-seasonal timescale. Despite progress in recent years, we are still lacking a process-based conceptual understanding of blocked regime dynamics, which hinders a better representation of blocks in numerical models. In particular the relative contributions of dry and moist processes in the onset and maintenance of a block remain unclear.Here we aim to revisit the dynamics of blocking in the Euro-Atlantic region. To this end we investigate the life cycles of blocked weather regimes from a potential vorticity (PV) perspective in ERA5 reanalysis data (from 1979 to present) from the European Centre for Medium-Range Weather Forecasts. We develop a diagnostic PV framework that allows the tracking of negative PV anomalies associated with blocked weather regimes. Complemented by piecewise PV-tendencies - separated into advective and diabatic PV tendencies - we are able to disentangle different physical processes affecting the amplitude evolution of negative PV anomalies associated with blocked regimes. Most importantly, this approach newly enables us to distinguish between the roles of dry and moist dynamics in the initiation and maintenance of blocked weather regimes in a common framework. A first application demonstrates the functionality of the developed PV framework and corroborates the importance of moist-diabatic processes in the initiation and maintenance of a block in a regime life cycle.&#160;

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