Implementation of a Digital Filter Initialization in the WRF Model and Its Application in the Rapid Refresh

Abstract Because of limitations of variational and ensemble data assimilation schemes, resulting analysis fields exhibit some noise from imbalance in subsequent model forecasts. Controlling finescale noise is desirable in the NOAA’s Rapid Refresh (RAP) assimilation/forecast system, which uses an hourly data assimilation cycle. Hence, a digital filter initialization (DFI) capability has been introduced into the Weather Research and Forecasting Model and applied operationally in the RAP, for which hourly intermittent assimilation makes DFI essential. A brief overview of the DFI approach, its implementation, and some of its advantages are discussed. Results from a 1-week impact test with and without DFI demonstrate that DFI is effective at reducing high-frequency noise in short-term operational forecasts as well as providing evidence of reduced errors in the 1-h mass and momentum fields. However, DFI is also shown to reduce the strength of parameterized deep moist convection during the first hour of the forecast.

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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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Convection Initiation Aided by Lake-Breeze Convergence over the Niagara Peninsula

Monthly Weather Review ◽

10.1175/mwr-d-19-0123.1 ◽

2019 ◽

Vol 147 (11) ◽

pp. 3955-3979 ◽

Cited By ~ 2

Author(s):

Chun-Chih Wang ◽

Daniel J. Kirshbaum ◽

David M. L. Sills

Keyword(s):

Large Scale ◽

Inversion Layer ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Lake Breeze ◽

Moist Convection ◽

Warm Advection ◽

American Science ◽

Convection Initiation ◽

Cloud Tops

Abstract Observations from the 2015 Environment and Climate Change Canada Pan/Parapan American Science Showcase (ECPASS) and real-case, cloud-resolving numerical simulations with the Weather Research and Forecasting (WRF) Model are used to investigate two cases of moist convection forced by lake-breeze convergence over southern Ontario (18 July and 15 August 2015). The two cases shared several characteristics, including high pressure conditions, similar morning soundings, and isolated afternoon convection along a line of lake-breeze convergence between Lakes Erie and Ontario. However, the convection was significantly stronger in the August case, with robustly deeper clouds and larger radar reflectivities than in the July case. Synoptic and mesoscale analyses of these events reveal that the key difference between them was their large-scale forcing. The July event exhibited a combination of strong warm advection and large-scale descent at midlevels (850–650 hPa), which created an inversion layer that capped cloud tops at 4–6 km. The August case exhibited similar features (large-scale descent and warm advection), but these were focused at higher levels (700–400 hPa) and weaker. As a consequence, the convection in the August case was less suppressed at midlevels and ascended deeper (reaching over 8 km). Although the subcloud updraft along the lake-breeze convergence zone was also found to be stronger in the August case, this difference was found to be an effect, rather than a cause, of stronger moist convection within the cloud layer.

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Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite’s MWHS-2 Data Assimilation

Remote Sensing ◽

10.3390/rs13224556 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4556

Author(s):

Dongmei Xu ◽

Xuewei Zhang ◽

Hong Li ◽

Haiying Wu ◽

Feifei Shen ◽

...

Keyword(s):

Data Assimilation ◽

Three Dimensional ◽

Wrf Model ◽

Background Field ◽

Weather Research And Forecasting ◽

Initial Field ◽

Parameterization Schemes ◽

Microwave Humidity Sounder ◽

Radiance Data ◽

Physical Parameterization

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.

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Improving the Four-Dimensional Incremental Analysis Update (4DIAU) with the HWRF 4DEnVar Data Assimilation System for Rapidly Evolving Hurricane Prediction

Monthly Weather Review ◽

10.1175/mwr-d-21-0068.1 ◽

2021 ◽

Author(s):

Xu Lu ◽

Xuguang Wang

Keyword(s):

Data Assimilation ◽

Inner Core ◽

Weather Research And Forecasting ◽

Incremental Analysis ◽

Short Term ◽

Linear Evolution ◽

Oscillation Model ◽

Incremental Update ◽

Hurricane Prediction ◽

Hwrf Model

AbstractShort-term spin-up for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU).In the traditional 4DIAU, increments are pre-determined by subtracting background forecasts from analyses. Such pre-determined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapid-evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with hurricane Patricia (2015).Results from both oscillation model and HWRF model show that: 1. the pre-determined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution. 2. 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the over-filtering. 3. Relocating initial background before the incremental update can improve the corresponding traditional methods. 4. the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments.

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The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

Advances in Meteorology ◽

10.1155/2011/803593 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Chien-Ben Chou ◽

Huei-Ping Huang

Keyword(s):

Data Assimilation ◽

Three Dimensional ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Atmospheric Infrared Sounder ◽

Background Error ◽

Forecasting System ◽

Typhoon Tracks ◽

The Impact ◽

Decorrelation Scale

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

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The Potential Impact of Assimilating Synthetic Microwave Radiances Onboard a Future Geostationary Satellite on the Prediction of Typhoon Lekima Using the WRF Model

Remote Sensing ◽

10.3390/rs13050886 ◽

2021 ◽

Vol 13 (5) ◽

pp. 886

Author(s):

Yuanbing Wang ◽

Jieying He ◽

Yaodeng Chen ◽

Jinzhong Min

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Wrf Model ◽

Geostationary Satellite ◽

Weather Research And Forecasting ◽

Observational System ◽

Weather Events ◽

High Impact Weather ◽

New Perspective ◽

Microwave Radiances

Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).

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Intercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)

Monthly Weather Review ◽

10.1175/mwr-d-13-00394.1 ◽

2014 ◽

Vol 142 (9) ◽

pp. 3347-3364 ◽

Cited By ~ 16

Author(s):

Jonathan Poterjoy ◽

Fuqing Zhang

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Hybrid System ◽

Ensemble Kalman Filter ◽

Computational Cost ◽

Wrf Model ◽

Variational Data Assimilation ◽

Weather Research And Forecasting ◽

Forecast Errors ◽

Verification Methods

This study examines the performance of ensemble and variational data assimilation systems for the Weather Research and Forecasting (WRF) Model. These methods include an ensemble Kalman filter (EnKF), an incremental four-dimensional variational data assimilation (4DVar) system, and a hybrid system that uses a two-way coupling between the two approaches (E4DVar). The three methods are applied to assimilate routinely collected data and field observations over a 10-day period that spans the life cycle of Hurricane Karl (2010), including the pregenesis disturbance that preceded its development into a tropical cyclone. In general, forecasts from the E4DVar analyses are found to produce smaller 48–72-h forecast errors than the benchmark EnKF and 4DVar methods for all variables and verification methods tested in this study. The improved representation of low- and midlevel moisture and vorticity in the E4DVar analyses leads to more accurate track and intensity predictions by this system. In particular, E4DVar analyses provide persistently more skillful genesis and rapid intensification forecasts than the EnKF and 4DVar methods during cycling. The data assimilation experiments also expose additional benefits of the hybrid system in terms of physical balance, computational cost, and the treatment of asynoptic observations near the beginning of the assimilation window. These factors make it a practical data assimilation method for mesoscale analysis and forecasting, and for tropical cyclone prediction.

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Implementation of the Unified Representation of Deep Moist Convection in the CWBGFS

Monthly Weather Review ◽

10.1175/mwr-d-21-0067.1 ◽

2021 ◽

Vol 149 (10) ◽

pp. 3525-3539

Author(s):

Chun-Yian Su ◽

Chien-Ming Wu ◽

Wei-Ting Chen ◽

Jen-Her Chen

Keyword(s):

Multiple Scales ◽

Horizontal Resolution ◽

Cumulus Parameterization ◽

Moist Convection ◽

Short Term ◽

Local Circulation ◽

Convective Systems ◽

Simulated Precipitation ◽

Deep Moist Convection ◽

Central Weather Bureau

AbstractThis study implements the unified parameterization (UP) in the Central Weather Bureau Global Forecast System (CWBGFS) based on the relaxed Arakawa–Schubert scheme (RAS) at a horizontal resolution of 15 km. The new cumulus parameterization that incorporates the UP framework is called URAS. The UP generalizes the representation of moist convection between the parameterized and the explicitly resolved processes according to the process-dependent convective updraft fraction (σ). Short-term hindcasts are performed to investigate the impacts of the UP on the simulated precipitation variability and organized convective systems over the Maritime Continent when multiple scales of convection occurred. The result shows that σ is generally larger when convective systems develop, which adaptively reduces the parameterized convection and increases the spatial variation of moisture. In the URAS experiment, the moisture hotspots within organized convective systems contribute to the enhanced local circulation and the more significant variability of precipitation. Consequently, the URAS has a more realistic precipitation spectrum, an improved relationship between the maximum precipitation and the horizontal scale of the convective systems, and an improved column water vapor–precipitation relationship.

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Evaluation of scale-aware convection schemes at the kilometer-scale resolution

10.5194/egusphere-egu2020-5337 ◽

2020 ◽

Author(s):

Xu Zhang ◽

Jian-Wen Bao ◽

Baode Chen

Keyword(s):

Deep Convection ◽

Wrf Model ◽

Horizontal Resolution ◽

Theory And Practice ◽

Coarse Grained ◽

Great Effort ◽

Moist Convection ◽

Direct Evaluation ◽

Deep Moist Convection ◽

Convection Schemes

Numerical weather predictions (NWP) models are increasingly run using kilometer-scale horizontal grid spacing at which convection is partially resolved and the use of a subgrid convection parameterization scheme is still required. Traditionally, subgrid deep convection has been represented by mass flux-based convection parameterizations based on the ensemble-mean closure concept. Recently, a great effort has been made to develop scale-aware subgrid convection schemes that can be used in kilometer-scale NWP models. However, direct evaluation of these schemes is rarely done using coarse-grained large-eddy simulation (LES).In this study, an idealized LES of deep moist convection is performed to assess the performance of three widely-used scale-aware subgrid convection schemes in the Weather Research and Forecast (WRF) model that is run at 3-km horizontal resolution. It is found that the simulations using the three schemes not only differ from each other but also do not converge to the coarse-grained LES, indicating that further investigation is required as to what &#8220;scale-awareness&#8221; means in theory and practice.

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Improving Operational Numerical Prediction of Afternoon Thunderstorms over Taiwan through Surface Data Assimilation

10.5194/egusphere-egu2020-7244 ◽

2020 ◽

Author(s):

I-Han Chen ◽

Jing-Shan Hong ◽

Ya-Ting Tsai ◽

Chin-Tzu Fong

Keyword(s):

Data Assimilation ◽

Radar Data ◽

Precipitation Forecast ◽

Cold Pool ◽

Moist Convection ◽

Convective Systems ◽

Forecast Lead Time ◽

Deep Moist Convection ◽

Outflow Boundary ◽

Surface Observations

Taiwan, a subtropical island with steep mountains, is influenced by diverse weather systems, including typhoons, monsoons, frontal, and convective systems. Of these, the prediction of deep, moist convection here is particularly challenging due to complex topography and apparent landsea contrast. This study explored the benefits of assimilating surface observations on prediction of afternoon thunderstorms using a 2-km resolution WRF and WRFDA model system with rapid update cycles. Consecutive afternoon thunderstorm events during 30 June to 08 July 2017 are selected. Five experiments, consisting of 240 continuous cycles are designed to evaluate the data assimilation strategy and observation impact. Statistical results show that assimilating surface observations systematically improves the accuracy of wind and temperature prediction near the surface. Also, assimilating surface observations alone in one-hour intervals improves model quantitative precipitation forecast (QPF) skill, extending the forecast lead time in the morning. Furthermore, radar data assimilation can benefit by the additional assimilation of surface observations, particularly for improving the model QPF skill for large rainfall thresholds. An afternoon thunderstorm event that occurred on 06 July 2017 is further examined. By assimilating surface and radar observations, the model is able to capture the timing and location of the convection. Consequently, the accuracy of the predicted cold pool and outflow boundary is improved, when compared to the surface observations.

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