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Xin Zhang
,
Ying-Hwa Kuo
,
Shu-Ya Chen
,
Xiang-Yu Huang
, and
Ling-Feng Hsiao

Abstract

The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

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Byoung-Joo Jung
,
Hyun Mee Kim
,
Thomas Auligné
,
Xin Zhang
,
Xiaoyan Zhang
, and
Xiang-Yu Huang

Abstract

An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%–70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.

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Craig S. Schwartz
,
Zhiquan Liu
,
Xiang-Yu Huang
,
Ying-Hwa Kuo
, and
Chin-Tzu Fong

Abstract

The Weather Research and Forecasting Model (WRF) “hybrid” variational-ensemble data assimilation (DA) algorithm was used to initialize WRF model forecasts of three tropical cyclones (TCs). The hybrid-initialized forecasts were compared to forecasts initialized by WRF's three-dimensional variational (3DVAR) DA system. An ensemble adjustment Kalman filter (EAKF) updated a 32-member WRF-based ensemble system that provided flow-dependent background error covariances for the hybrid. The 3DVAR, hybrid, and EAKF configurations cycled continuously for ~3.5 weeks and produced new analyses every 6 h that initialized 72-h WRF forecasts with 45-km horizontal grid spacing. Additionally, the impact of employing a TC relocation technique and using multiple outer loops (OLs) in the 3DVAR and hybrid minimizations were explored.

Model output was compared to conventional, dropwindsonde, and TC “best track” observations. On average, the hybrid produced superior forecasts compared to 3DVAR when only one OL was used during minimization. However, when three OLs were employed, 3DVAR forecasts were dramatically improved but the mean hybrid performance changed little. Additionally, incorporation of TC relocation within the cycling systems further improved the mean 3DVAR-initialized forecasts but the average hybrid-initialized forecasts were nearly unchanged.

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Kathryn M. Newman
,
Craig S. Schwartz
,
Zhiquan Liu
,
Hui Shao
, and
Xiang-Yu Huang

Abstract

This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.

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Feng Gao
,
Xiaoyan Zhang
,
Neil A. Jacobs
,
Xiang-Yu Huang
,
Xin Zhang
, and
Peter P. Childs

Abstract

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.

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Qingnong Xiao
,
Ying-Hwa Kuo
,
Zaizhong Ma
,
Wei Huang
,
Xiang-Yu Huang
,
Xiaoyan Zhang
,
Dale M. Barker
,
John Michalakes
, and
Jimy Dudhia

Abstract

The tangent linear and adjoint of an adiabatic version of the Weather Research and Forecasting (WRF) Model with its Advanced Research WRF (ARW) dynamic core have been developed. The source-to-source automatic differentiation tool [i.e., the Transformation of Algorithm (TAF) in FORTRAN] was used in the development. Tangent linear and adjoint checks of the developed adiabatic WRF adjoint modeling system (WAMS) were conducted, and all necessary correctness verification procedures were passed. As the first application, the adiabatic WAMS was used to study the adjoint sensitivity of a severe windstorm in Antarctica. Linearity tests indicated that an adjoint-based sensitivity study with the Antarctic Mesoscale Prediction System (AMPS) 90-km domain configuration for the windstorm is valid up to 24 h. The adjoint-based sensitivity calculation with adiabatic WAMS identified sensitive regions for the improvement of the 24-h forecast of the windstorm. It is indicated that the windstorm forecast largely relies on the model initial conditions in the area from the south part of the Trans-Antarctic Mountains to West Antarctica and between the Ross Ice Shelf and the South Pole. Based on the sensitivity analysis, the southerly or southeasterly wind at lower levels in the sensitivity region should be larger, the cyclone should be stronger, and the atmospheric stratification should be more stable over the north slope of the Trans-Antarctic Mountain to the Ross Ice Shelf, than the AMPS analyses. By constructing pseudo-observations in the sensitivity region using the gradient information of forecast windstorm intensity around McMurdo, the model initial conditions are revised with the WRF three-dimensional variational data assimilation, which leads to significant improvement in the prediction of the windstorm. An adjoint sensitivity study is an efficient way to identify sensitivity regions in order to collect more observations in the region for better forecasts in a specific aspect of interest.

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Yaodeng Chen
,
Hongli Wang
,
Jinzhong Min
,
Xiang-Yu Huang
,
Patrick Minnis
,
Ruizhi Zhang
,
Julie Haggerty
, and
Rabindra Palikonda

Abstract

Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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Hui Shao
,
John Derber
,
Xiang-Yu Huang
,
Ming Hu
,
Kathryn Newman
,
Donald Stark
,
Michael Lueken
,
Chunhua Zhou
,
Louisa Nance
,
Ying-Hwa Kuo
, and
Barbara Brown

Abstract

With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.

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Xiangming Sun
,
Xiang-Yu Huang
,
Chris Gordon
,
Marion Mittermaier
,
Rebecca Beckett
,
Wee Kiong Cheong
,
Dale Barker
,
Rachel North
, and
Allison Semple

Abstract

Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Peninsular Malaysia. The performance of forecasts for 63 past squall events is evaluated using a subjective evaluation by forecasters and an objective evaluation based on the fractions skill score (FSS). The purpose of this study is to investigate whether an objective procedure can reproduce the main results of the subjective evaluation. A convection permitting version of the Met Office (UKMO) Unified Model (UM), configured for a limited domain in the southern region of the South China Sea, is used with two driving global deterministic models: the UM and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Subjective and objective evaluation scoring methods for the two limited-area forecasts of the UM are compared, and it is shown that the objective procedure can reasonably emulate the scores produced by the forecasters in the context of parameters that are of direct relevance to the forecast process. This indicates that automated objective verification methods may be a reasonable alternative to resource intensive subjective evaluations for some cases. The robustness of the objective results is investigated using 7 months of data, and issues of statistical significance are considered.

Open access
Ling-Feng Hsiao
,
Xiang-Yu Huang
,
Ying-Hwa Kuo
,
Der-Song Chen
,
Hongli Wang
,
Chin-Cheng Tsai
,
Tien-Chiang Yeh
,
Jing-Shan Hong
,
Chin-Tzu Fong
, and
Cheng-Shang Lee

Abstract

A blending method to merge the NCEP global analysis with the regional analysis from the WRF variational data assimilation system is implemented using a spatial filter for the purpose of initializing the Typhoon WRF (TWRF) Model, which has been in operation at Taiwan’s Central Weather Bureau (CWB) since 2010. The blended analysis is weighted toward the NCEP global analysis for scales greater than the cutoff length of 1200 km, and is weighted toward the WRF regional analysis for length below that. TWRF forecast experiments on 19 typhoons from July to October 2013 over the western North Pacific Ocean show that the large-scale analysis from NCEP GFS is superior to that of the regional analysis, which significantly improves the typhoon track forecasts. On the other hand, the regional WRF analysis provides a well-developed typhoon structure and more accurately captures the influence of the Taiwan topography on the typhoon circulation. As a result, the blended analysis takes advantage of the large-scale analysis from the NCEP global analysis and the detailed mesoscale analysis from the regional WRF analysis. In additional to the improved track forecast, the blended analysis also provides more accurate rainfall forecasts for typhoons affecting Taiwan. Because of the improved performance, the blending method has been implemented in the CWB operational TWRF typhoon prediction system.

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