Evaluation and Bias Correction in WRF Model Forecasting of Precipitation and Potential Evapotranspiration

Abstract A reliable forecast of potential evapotranspiration (ET0) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET0 and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET0 using the Penman–Monteith (PM) approach, as well as calculated ET0 and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM–ET0. Local Ws bias correction using observations on days preceding the forecast and interpolation of the resulting PM–ET0 to other locations led to significant improvement in ET0 forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET0 forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model–observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET0, the WRF Model was found to be a high-potential tool for improving crop irrigation management.

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Simulation of Severe Thunderstorms in Conjugation of Westerly and Easterly on 31 March 2019 in Bangladesh

Journal of Engineering Science ◽

10.3329/jes.v12i3.57477 ◽

2022 ◽

Vol 12 (3) ◽

pp. 29-43

Author(s):

Samarendra Karmakar ◽

Mohan Kumar Das ◽

Md Quamrul Hassam ◽

Md Abdul Mannan

Keyword(s):

South Asian ◽

Wrf Model ◽

Horizontal Resolution ◽

Weather Research And Forecasting ◽

Atmospheric Conditions ◽

Microphysics Scheme ◽

Severe Thunderstorms ◽

Synoptic Conditions ◽

Pbl Scheme ◽

Single Moment

The diagnostic and prognostic studies of thunderstorms/squalls are very important to save live and loss of properties. The present study aims at diagnose the different tropospheric parameters, instability and synoptic conditions associated the severe thunderstorms with squalls, which occurred at different places in Bangladesh on 31 March 2019. For prognostic purposes, the severe thunderstorms occurred on 31 March 2019 have been numerically simulated. In this regard, the Weather Research and Forecasting (WRF) model is used to predict different atmospheric conditions associated with the severe storms. The study domain is selected for 9 km horizontal resolution, which almost covers the south Asian region. Numerical experiments have been conducted with the combination of WRF single-moment 6 class (WSM6) microphysics scheme with Yonsei University (YSU) PBL scheme in simulation of the squall events. Model simulated results are compared with the available observations. The observed values of CAPE at Kolkata both at 0000 and 1200 UTC were 2680.4 and 3039.9 J kg-1 respectively on 31 March 2019 and are found to be comparable with the simulated values. The area averaged actual rainfall for 24 hrs is found is 22.4 mm, which complies with the simulated rainfall of 20-25 mm for 24 hrs. Journal of Engineering Science 12(3), 2021, 29-43

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On the effective resolution of WRF simulations at microscale grid resolution.

10.5194/egusphere-egu21-229 ◽

2021 ◽

Author(s):

Pedro Bolgiani ◽

Javier Díaz-Fernández ◽

Lara Quitián-Hernández ◽

Mariano Sastre ◽

Daniel Santos-Muñoz ◽

...

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Wrf Model ◽

Research Community ◽

Weather Research And Forecasting ◽

Large Uncertainty ◽

Partial Explanation ◽

Synoptic Conditions ◽

Large Effort ◽

Numerical Weather Prediction Models

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

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Two Overlooked Biases of the Advanced Research WRF (ARW) Model in Geopotential Height and Temperature

Monthly Weather Review ◽

10.1175/mwr-d-12-00045.1 ◽

2012 ◽

Vol 140 (12) ◽

pp. 3907-3918 ◽

Cited By ~ 10

Author(s):

Tae-Kwon Wee ◽

Ying-Hwa Kuo ◽

Dong-Kyou Lee ◽

Zhiquan Liu ◽

Wei Wang ◽

...

Keyword(s):

Potential Temperature ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Initial Condition ◽

Forecast Bias ◽

Hydrostatic Equation ◽

Arw Model ◽

Occultation Data ◽

Bias Corrections

Abstract The authors have discovered two sizeable biases in the Weather Research and Forecasting (WRF) model: a negative bias in geopotential and a warm bias in temperature, appearing both in the initial condition and the forecast. The biases increase with height and thus manifest themselves at the upper part of the model domain. Both biases stem from a common root, which is that vertical structures of specific volume and potential temperature are convex functions. The geopotential bias is caused by the particular discrete hydrostatic equation used in WRF and is proportional to the square of the thickness of model layers. For the vertical levels used in this study, the bias far exceeds the gross 1-day forecast bias combining all other sources. The bias is fixed by revising the discrete hydrostatic equation. WRF interpolates potential temperature from the grids of an external dataset to the WRF grids in generating the initial condition. Associated with the Exner function, this leads to the marked bias in temperature. By interpolating temperature to the WRF grids and then computing potential temperature, the bias is removed. The bias corrections developed in this study are expected to reduce the disparity between the forecast and observations, and eventually to improve the quality of analysis and forecast in the subsequent data assimilation. The bias corrections might be especially beneficial to assimilating height-based observations (e.g., radio occultation data).

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Extreme Rainfall Forecast with the WRF-ARW Model in the Central Andes of Peru

Atmosphere ◽

10.3390/atmos9090362 ◽

2018 ◽

Vol 9 (9) ◽

pp. 362 ◽

Cited By ~ 17

Author(s):

Aldo Moya-Álvarez ◽

José Gálvez ◽

Andrea Holguín ◽

René Estevan ◽

Shailendra Kumar ◽

...

Keyword(s):

Extreme Event ◽

Wrf Model ◽

Extreme Rainfall ◽

Central Andes ◽

Weather Research And Forecasting ◽

Mountain Range ◽

Microphysics Scheme ◽

Vertical Movements ◽

Synoptic Conditions ◽

Weather Research

The ability of the WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to forecast extreme rainfall in the Central Andes of Peru is evaluated in this study, using observations from stations located in the Mantaro basin and GOES (Geostationary Operational Environmental Satellite) images. The evaluation analyzes the synoptic conditions averaged over 40 extreme event cases, and considers model simulations organized in 4 nested domains. We first establish that atypical events in the region are those with more than 27 mm of rainfall per day when averaging over all the stations. More than 50% of the selected cases occurred during January, February, and April, with the most extreme occurring during February. The average synoptic conditions show negative geopotential anomalies and positive humidity anomalies in 700 and 500 hPa. At 200 hPa, the subtropical upper ridge or “Bolivian high” was present, with its northern divergent flank over the Mantaro basin. Simulation results show that the Weather Research and Forecasting (WRF) model underestimates rainfall totals in approximately 50–60% of cases, mainly in the south of the basin and in the extreme west along the mountain range. The analysis of two case studies shows that the underestimation by the model is probably due to three reasons: inability to generate convection in the upstream Amazon during early morning hours, apparently related to processes of larger scales; limitations on describing mesoscale processes that lead to vertical movements capable of producing extreme rainfall; and limitations on the microphysics scheme to generate heavy rainfall.

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Weighing Lysimeters for Developing Crop Coefficients and Efficient Irrigation Practices for Vegetable Crops

HortScience ◽

10.21273/hortsci.45.11.1597 ◽

2010 ◽

Vol 45 (11) ◽

pp. 1597-1604 ◽

Cited By ~ 29

Author(s):

David R. Bryla ◽

Thomas J. Trout ◽

James E. Ayars

Keyword(s):

Potential Evapotranspiration ◽

Irrigation Management ◽

Quadratic Function ◽

Ground Cover ◽

Soil Surface ◽

Irrigation Scheduling ◽

Yield Potential ◽

Vegetable Crops ◽

Soil Water Conditions ◽

Crop Coefficients

Large, precision weighing lysimeters are expensive but invaluable tools for measuring crop evapotranspiration and developing crop coefficients. Crop coefficients are used by both growers and researchers to estimate crop water use and accurately schedule irrigations. Two lysimeters of this type were installed in 2002 in central California to determine daily rates of crop and potential (grass) evapotranspiration and develop crop coefficients for better irrigation management of vegetable crops. From 2002 to 2006, the crop lysimeter was planted with broccoli, iceberg lettuce, bell pepper, and garlic. Basal crop coefficients, Kcb, defined as the ratio of crop to potential evapotranspiration when the soil surface is dry but transpiration in unlimited by soil water conditions, increased as a linear or quadratic function of the percentage of ground covered by vegetation. At midseason, when groundcover was greater than 70% to 90%, Kcb was ≈1.0 in broccoli, 0.95 in lettuce, and 1.1 in pepper, and Kcb of each remained the same until harvest. Garlic Kcb, in comparison, increased to 1.0 by the time the crop reached 80% ground cover, but with only 7% of additional coverage, Kcb continued to increase to 1.3, until irrigation was stopped to dry the crop for harvest. Three weeks after irrigation was cutoff, garlic Kcb declined rapidly to a value of 0.16 by harvest. Yields of each crop equaled or exceeded commercial averages for California with much less water in some cases than typically applied. The new crop coefficients will facilitate irrigation scheduling in the crops and help to achieve full yield potential without overirrigation.

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The Influence of Assimilated Upstream, Preconvective Dropsonde Observations on Ensemble Forecasts of Convection Initiation during the Mesoscale Predictability Experiment

Monthly Weather Review ◽

10.1175/mwr-d-17-0159.1 ◽

2017 ◽

Vol 145 (12) ◽

pp. 4747-4770 ◽

Cited By ~ 3

Author(s):

Alexandra M. Keclik ◽

Clark Evans ◽

Paul J. Roebber ◽

Glen S. Romine

Keyword(s):

Short Range ◽

Wrf Model ◽

Skill Score ◽

Forecast Skill ◽

Weather Research And Forecasting ◽

Moist Convection ◽

Ensemble Forecasts ◽

High Bias ◽

Deep Moist Convection ◽

Convection Initiation

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso- α- to synoptic-scale observations collected in upstream, preconvective environments is insufficient to improve short-range ensemble convection initiation (CI) forecast skill over the set of cases considered by the 2013 Mesoscale Predictability Experiment (MPEX) because of a limited influence upon the lower-tropospheric phenomena that modulate CI occurrence, timing, and location. The ensemble Kalman filter implementation within the Data Assimilation Research Testbed as coupled to the Advanced Research Weather Research and Forecasting (WRF) Model is used to initialize two nearly identical 30-member ensembles of short-range forecasts for each case: one initial condition set that incorporates MPEX dropsonde observations and one that excludes these observations. All forecasts for a given mission begin at 1500 UTC and are integrated for 15 h on a convection-permitting grid encompassing much of the conterminous United States. Forecast verification is conducted probabilistically using fractions skill score and deterministically using a 2 × 2 contingency table approach at multiple neighborhood sizes and spatiotemporal event-matching thresholds to assess forecast skill and support hypothesis testing. The probabilistic verification represents the first of its kind for numerical CI forecasts. Forecasts without MPEX observations have high fractions skill score and probabilities of detection on the meso- α scale but exhibit a considerable high bias for forecast CI event count. Assimilating MPEX observations has a negligible impact upon forecast skill for the cases considered, independent of verification metric, as the MPEX observations result in only subtle differences primarily manifest in the position and intensity of atmospheric features responsible for focusing and/or triggering deep, moist convection.

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Astronomical seeing forecast for the Thai National Observatory

Journal of Physics Conference Series ◽

10.1088/1742-6596/2145/1/012015 ◽

2021 ◽

Vol 2145 (1) ◽

pp. 012015

Author(s):

Ronald Macatangay ◽

Somsawat Rattanasoon

Keyword(s):

Bias Correction ◽

Wrf Model ◽

Image Motion ◽

Weather Research And Forecasting ◽

National Observatory ◽

Chiang Mai ◽

Doi Inthanon ◽

Astronomical Seeing ◽

Weather Research

Abstract Forecasting the astronomical seeing above an observatory can assist astronomers plan their observations. In this study, the astronomical seeing above the Thai National Observatory (TNO) in Doi Inthanon, Chiang Mai, Thailand was simulated using the Weather Research and Forecasting (WRF) model. The model outputs were then compared to Polaris seeing observations and using the Differential Image Motion Monitor (DIMM). Results showed that the forecasts capture the variation of the astronomical seeing fairly well. However, bias correction is needed on the simulations due to lack of data from meteorological balloons to constrain the model.

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Citrus Irrigation Management

EDIS ◽

10.32473/edis-ss660-2017 ◽

2017 ◽

Vol 2017 (5) ◽

Author(s):

Davie Mayeso Kadyampakeni ◽

Kelly T. Morgan ◽

Mongi Zekri ◽

Rhuanito Ferrarezi ◽

Arnold Schumann ◽

...

Keyword(s):

Water Stress ◽

Physiological Activity ◽

Irrigation Management ◽

Fruit Size ◽

Irrigation Scheduling ◽

Leaf Expansion ◽

Tree Canopy ◽

Limiting Factor ◽

Irrigation Frequency ◽

Citrus Production

Water is a limiting factor in Florida citrus production during the majority of the year because of the low water holding capacity of sandy soils resulting from low clay and the non-uniform distribution of the rainfall. In Florida, the major portion of rainfall comes in June through September. However, rainfall is scarce during the dry period from February through May, which coincides with the critical stages of bloom, leaf expansion, fruit set, and fruit enlargement. Irrigation is practiced to provide water when rainfall is not sufficient or timely to meet water needs. Proper irrigation scheduling is the application of water to crops only when needed and only in the amounts needed; that is, determining when to irrigate and how much water to apply. With proper irrigation scheduling, yield will not be limited by water stress. With citrus greening (HLB), irrigation scheduling is becoming more important and critical and growers cannot afford water stress or water excess. Any degree of water stress or imbalance can produce a deleterious change in physiological activity of growth and production of citrus trees. The number of fruit, fruit size, and tree canopy are reduced and premature fruit drop is increased with water stress. Extension growth in shoots and roots and leaf expansion are all negatively impacted by water stress. Other benefits of proper irrigation scheduling include reduced loss of nutrients from leaching as a result of excess water applications and reduced pollution of groundwater or surface waters from the leaching of nutrients. Recent studies have shown that for HLB-affected trees, irrigation frequency should increase and irrigation amounts should decrease to minimize water stress from drought stress or water excess, while ensuring optimal water availability in the rootzone at all times.

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Irrigation Scheduling with Soil Gas Diffusivity as a Decision Tool to Mitigate N2O Emissions from a Urine-Affected Pasture

Agriculture ◽

10.3390/agriculture11050443 ◽

2021 ◽

Vol 11 (5) ◽

pp. 443

Author(s):

Camille Rousset ◽

Timothy J. Clough ◽

Peter R. Grace ◽

David W. Rowlings ◽

Clemens Scheer

Keyword(s):

Irrigation Management ◽

Irrigation Treatment ◽

Irrigation Scheduling ◽

Soil Gas ◽

N2o Emissions ◽

Irrigation Frequency ◽

Water Demands ◽

Gas Diffusivity ◽

Matter Production ◽

Access To Water

Pastures require year-round access to water and in some locations rely on irrigation during dry periods. Currently, there is a dearth of knowledge about the potential for using irrigation to mitigate N2O emissions. This study aimed to mitigate N2O losses from intensely managed pastures by adjusting irrigation frequency using soil gas diffusivity (Dp/Do) thresholds. Two irrigation regimes were compared; a standard irrigation treatment based on farmer practice (15 mm applied every 3 days) versus an optimised irrigation treatment where irrigation was applied when soil Dp/Do was ≈0.033 (equivalent to 50% of plant available water). Cow urine was applied at a rate of 700 kg N ha−1 to simulate a ruminant urine deposition event. In addition to N2O fluxes, soil moisture content was monitored hourly, Dp/Do was modelled, and pasture dry matter production was measured. Standard irrigation practices resulted in higher (p = 0.09) cumulative N2O emissions than the optimised irrigation treatment. Pasture growth rates under treatments did not differ. Denitrification during re-wetting events (irrigation and rain) contributed to soil N2O emissions. These results warrant further modelling of irrigation management as a mitigation option for N2O emissions from pasture soils, based on Dp/Do thresholds, rainfall, plant water demands and evapotranspiration.

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