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Shu-Chih Yang
,
Shu-Hua Chen
,
Shu-Ya Chen
,
Ching-Yuang Huang
, and
Ching-Sen Chen

Abstract

Global positioning system (GPS) radio occultation (RO) data have been broadly used in global and regional numerical weather predictions. Assimilation with the bending angle often performs better than refractivity, which is inverted from the bending angle under spherical assumption and is sometimes associated with negative biases at the lower troposphere; however, the bending angle operator also requires a higher model top as used in global models. This study furnishes the feasibility of bending-angle assimilation in the prediction of heavy precipitation systems with a regional model. The local RO operators for simulating bending angle and refractivity are implemented in the Weather Research and Forecasting (WRF)–local ensemble transform Kalman filter (LETKF) framework. The impacts of assimilating RO data from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) using both operators are evaluated on the prediction of a heavy precipitation episode during Southwest Monsoon Experiment intensive observing period 8 (SoWMEX-IOP8) in 2008. Results show that both the refractivity and bending angle provide a favorable condition for generating this heavy rainfall event. In comparison with the refractivity data, the advantage of assimilating the bending angle is identified in the midtroposphere for deepening of the moist layer that leads to a rainfall forecast closer to the observations.

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Shu-Ya Chen
,
Ying-Hwa Kuo
, and
Ching-Yuang Huang

Abstract

In this study, the impact of global positioning system (GPS) radio occultation (RO) data on the prediction of the genesis of 10 tropical cyclones over the western North Pacific Ocean is assessed. With the use of a nonlocal excess phase observation operator in cycling data assimilation, the probability of detection for tropical cyclogenesis is increased from 30% to 70% for the cases considered, all of which developed into typhoons. However, the probability of detection is only increased to 40% when a local observation operator is used, indicating that the observation operator can significantly influence the performance of RO data assimilation in capturing tropical cyclogenesis. A nonlocal excess phase operator, which considers the atmospheric horizontal gradients by integrating the refractivity along a ray path, gives superior performance over the local observation operator. Additional sensitivity experiments on 3 of the 10 typhoon cases show that the RO data in the vicinity of the incipient cyclones (within 500 km of the cyclone center) are most critical to successful cyclogenesis prediction. This reflects the fact that having good RO observations at the right time and place is critical for RO to have beneficial impacts on tropical cyclogenesis. Further analyses for Typhoon Nuri (2008) show that assimilation of RO data using the nonlocal operator leads to moistening of the lower and middle troposphere, organized convection, robust grid-scale vertical motions, and the development of midlevel relative vorticity, all of which are favorable for tropical cyclogenesis.

Open access
Shu-Ya Chen
,
Ching-Yuang Huang
,
Ying-Hwa Kuo
, and
Sergey Sokolovskiy

Abstract

The Global Positioning System (GPS) radio occultation (RO) technique is becoming a robust global observing system. GPS RO refractivity is typically modeled at the ray perigee point by a “local refractivity operator” in a data assimilation system. Such modeling does not take into account the horizontal gradients that affect the GPS RO refractivity. A new observable (linear excess phase), defined as an integral of the refractivity along some fixed ray path within the model domain, has been developed in earlier studies to account for the effect of horizontal gradients.

In this study, the error statistics of both observables (refractivity and linear excess phase) are estimated using the GPS RO data from the Formosa Satellite 3–Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) mission. The National Meteorological Center (NMC) method, which is based on lagged forecast differences, is applied for evaluation of the model forecast errors that are used for estimation of the GPS RO observational errors. Also used are Weather Research and Forecasting (WRF) model forecasts in the East Asia region at 45-km resolution for one winter month (mid-January to mid-February) and one summer month (mid-August to mid-September) in 2007.

Fractional standard deviations of the observational errors of refractivity and linear excess phase both show an approximately linear decrease with height in the troposphere and a slight increase above the tropopause; their maximum magnitude is about 2.2% (2.5%) for refractivity and 1.1% (1.3%) for linear excess phase in the lowest 2 km for the winter (summer) month. An increase of both fractional observational errors near the surface in the summer month is attributed mainly to a larger amount of water vapor. The results indicate that the fractional observational error of refractivity is about twice as large as that of linear excess phase, regardless of season. The observational errors of both linear excess phase and refractivity are much less latitude dependent for summer than for winter. This difference is attributed to larger latitudinal variations of the specific humidity in winter.

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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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Shu-Ya Chen
,
Tae-Kwon Wee
,
Ying-Hwa Kuo
, and
David H. Bromwich

Abstract

The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

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Ching-Yuang Huang
,
Jia-Yang Lin
,
William C. Skamarock
, and
Shu-Ya Chen

Abstract

A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

Open access
Shu-Ya Chen
,
Cheng-Peng Shih
,
Ching-Yuang Huang
, and
Wen-Hsin Teng

Abstract

Conventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multiresolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast. Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60–15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-1 potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for two other typhoons using the MPAS-GSI system.

Open access
Ching-Yuang Huang
,
Shu-Ya Chen
,
S. K. A. V. Prasad Rao Anisetty
,
Shu-Chih Yang
, and
Ling-Feng Hsiao

Abstract

The impact of global positioning system (GPS) radio occultation (RO) soundings on the prediction of severe mei-yu frontal rainfall near Taiwan in June 2012 was investigated in this study using a developed local bending angle (LBA) operator. Two operators for local refractivity (REF) and nonlocal refractivity [excess phase (EPH)] were also used for comparisons. The devised LBA simplifies the calculation of the Abel transform in inverting model local refractivity without a loss of accuracy. These operators have been implemented into the three-dimensional variational data assimilation system of the Weather Research and Forecasting (WRF) Model to assimilate GPS RO soundings available from the Formosa Satellite Mission 3/Constellation Observing Systems for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC). The RO data are found to be beneficial to the WRF forecast of local severe rainfall in Taiwan. Characteristics of assimilation performance and innovation for the three operators are discussed. Both of the local operators performing assimilation at observation levels appear to produce mostly larger positive moisture increments than do the current nonlocal operators performing assimilation on the mean height of each model vertical level. As the information of the initial increments has propagated farther south with the frontal flow, the simulation for LBA shows better prediction of rainfall peaks in Taiwan on the second day than both REF and EPH, with a maximum improvement of about 25%. The positive impact of the RO data results partially from several RO observations near Mongolia and north China. This study provides an intercomparison among the three RO operators, and shows the feasibility of regional assimilation with LBA.

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Chuan-Chi Tu
,
Yi-Leng Chen
,
Shu-Ya Chen
,
Ying-Hwa Kuo
, and
Pay-Liam Lin

Abstract

A cycling run, which began 36 h before the model forecast, was employed to assimilate special Terrain-influenced Monsoon Rainfall Experiment (TiMREX) soundings, Global Telecommunications System (GTS) data, and Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) global positioning system (GPS) radio occultation (RO) refractivity profiles to improve the model initial conditions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) to study a coastal, heavy rainfall event over southwestern Taiwan during 15–16 June 2008. The 36-h cycling run with data assimilation (DA_ALL_DATA run) has a positive impact on the depiction of subsynoptic flow in the model initial conditions at 1200 UTC 15 June, including the warm moist tongue and southwesterly monsoon flow over the open ocean. Furthermore, the cold pool caused by the evaporative cooling of antecedent rains and orographic blocking over southwestern Taiwan are better resolved in the nested high-resolution domain in the DA_ALL_DATA run as compared to the initial conditions provided by the NCEP GFS. As a result, the heavy rainfall along the southwestern coast and afternoon localized heavy rainfall over northern Taiwan are better predicted in the DA_ALL_DATA run.

Model sensitivity tests are also performed to diagnose the effects of terrain and rain-evaporative cooling on the intensity and depth of the cold pool and degree of orographic blocking on the southwesterly flow over southwestern Taiwan. It is apparent that including rain-evaporative cooling from antecedent rains and orographic effects in the model initial conditions are important to account for the predicted rainfall distribution of this coastal rainfall event.

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Tae-Kwon Wee
,
Ying-Hwa Kuo
,
Dong-Kyou Lee
,
Zhiquan Liu
,
Wei Wang
, and
Shu-Ya Chen

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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