Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

Müller, Omar V.; Lovino, Miguel A.; Berbery, Ernesto Hugo

doi:10.1175/waf-d-15-0130.1

Cited by 16 publications

(11 citation statements)

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“…To successfully deal with this projected development, sustainable management of available water resources is mandatory. For improved decision support in regional water management or agriculture, high-quality regionalized weather and climate forecasting increasingly matters (Soares et al, 2012;Müller et al, 2016) and is a key requirement for successful hydrological and crop impact modeling (Decharme and Douville, 2006;Barbosa and Lakshmi Kumar, 2016;Parkes et al, 2019). Especially in semiarid regions and areas affected by El-Niño-Southern-Oscillation (ENSO) more profound knowledge of variations in seasonal rainfall, estimated streamflow and crop yield from regionalized model output, can help to better prepare against climate extremes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

Portele¹,

Laux²,

Lorenz³

et al. 2021

Front. Earth Sci.

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For climate adaptation and risk mitigation, decision makers in water management or agriculture increasingly demand for regionalized weather and climate information. To provide these, regional atmospheric models, such as the Weather Research and Forecasting (WRF) model, need to be optimized in their physical setup to the region of interest. The objective of this study is to evaluate four cumulus physics (CU), two microphysics (MP), two planetary boundary layer physics (PBL), and two radiation physics (RA) schemes in WRF according to their performance in dynamically downscaling the precipitation over two typical South American regions: one orographically complex area in Ecuador/Peru (horizontal resolution up to 9 and 3 km), and one area of rolling hills in Northeast Brazil (up to 9 km). For this, an extensive ensemble of 32 simulations over two continuous years was conducted. Including the reference uncertainty of three high-resolution global datasets (CHIRPS, MSWEP, ERA5-Land), we show that different parameterization setups can produce up to four times the monthly reference precipitation. This underscores the urgent need to conduct parameterization sensitivity studies before weather forecasts or input for impact modeling can be produced. Contrarily to usual studies, we focus on distributional, temporal and spatial precipitation patterns and evaluate these in an ensemble-tailored approach. These ensemble characteristics such as ensemble Structure-, Amplitude-, and Location-error, allow us to generalize the impacts of combining one parameterization scheme with others. We find that varying the CU and RA schemes stronger affects the WRF performance than varying the MP or PBL schemes. This effect is even present in the convection-resolving 3-km-domain over Ecuador/Peru where CU schemes are only used in the parent domain of the one-way nesting approach. The G3D CU physics ensemble best represents the CHIRPS probability distribution in the 9-km-domains. However, spatial and temporal patterns of CHIRPS are best captured by Tiedtke or BMJ CU schemes. Ecuadorian station data in the 3-km-domain is best simulated by the ensemble whose parent domains use the KF CU scheme. Accounting for all evaluation metrics, no general-purpose setup could be identified, but suited parameterizations can be narrowed down according to final application needs.

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Section: Introductionmentioning

confidence: 99%

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

Portele¹,

Laux²,

Lorenz³

et al. 2021

Front. Earth Sci.

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“…For relative humidity, the best were 2, 13, 14, 9, and 1. For mean temperature, the best performance was obtained with 14,2,22,10,and 19. For maximum temperature,14,20,22,2, and 10 perform better.…”

Section: Selection Of the Best Configurationmentioning

confidence: 99%

“…In Figure 7, we show the performance of all the configurations in a box plot for ten stations. We also include lines for the three best configurations (lower bias) in wind speed that have acceptable results [44] in the stations around the Baker River (stations 3,4,8,9,10).…”

Section: Wind Speedmentioning

confidence: 99%

“…Besides, there are different studies in the area that dynamically downscale reanalysis data to study specific events or processes in higher resolution. Examples of the dynamic downscaling process from coarse global to high resolution (<15 km) regional climate using NWP in South America are [10] that downscaled Global Forecast System (GFS) analysis data for most of South America, Villarroel et al [11] which was also used in [6] (4 km). Yáñez-Morroni et al [12] used NWP for forecasting precipitation in complex topography in the "Quebrada San Ramón" near Santiago in central Chile.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Somos‐Valenzuela

Manquehual-Cheuque

2020

Atmosphere

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The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

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“…e CHIRPS estimations also gained attention as input for climate monitoring tools. Six countries in southern South America have established the World Meteorological Organization Regional Climate Center (RCC-SSA) that involves a strong collaboration between weather services and academic institutions [26]. e RCC-SSA periodically generates CHIRPS precipitation estimates maps on pentadal and monthly time steps, providing relevant information for decision-making needed for agricultural activities and water management purposes.…”

Section: Introductionmentioning

confidence: 99%

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Rivera

Hinrichs

Marianetti

2019

Advances in Meteorology

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e Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset was conceived as a tool for monitoring drought and environmental change over land. Recent validation efforts along South America have assessed its suitability for reproducing the main spatial and temporal features of precipitation. Nevertheless, little has been done regarding the ability of CHIRPS for the assessment of wet and dry conditions, particularly in areas where in situ precipitation records are scarce. In this paper, we investigated the performance of CHIRPS for monitoring wet and dry events along the semiarid Central-Western Argentina. Using the Standardized Precipitation Index (SPI), we compared the CHIRPS database with records from 49 meteorological stations along the study area for the period 1987-2016. Results indicate that the CHIRPS dataset adequately reproduced the temporal variability of SPI on multiple timescales (1 month, 3 months, and 6 months), particularly in the region dominated by warm season precipitation. e large overestimation of the seasonal precipitation in the region dominated by cold season precipitation can introduce errors that are reflected in the performance of CHIRPS over the western portion of the domain. e frequency of wet and dry classes was accurately reproduced by CHIRPS on timescales larger than 1 month (SPI1), given the existence of a wet bias that produces an underestimation of the frequency of zero values. is bias is further translated to the evaluation of the SPI1 during the spatial and temporal assessment of historical dry (1998) and wet (2016) events, especially for the classification of extreme dry/wet months. e results from the evaluation indicate that CHIRPS is a suitable tool for assessing dry and wet conditions for timescales longer than 1 month and can support decision-making process within the hydrometeorological agencies over the region.

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Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

Cited by 16 publications

References 50 publications

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

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