Evaluation and Assimilation of the COSMIC‐2 Radio Occultation Constellation Observed Atmospheric Refractivity in the WRF Data Assimilation System

Singh, Randhir; Ojha, Satya P.; Anthes, Richard A.; Hunt, Douglas

doi:10.1029/2021jd034935

Cited by 9 publications

(13 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, many forward observation operators potential of improvement. Singh et al (2019), Singh et al (2021 introduced an improved observation operator which takes also hydrometeors into account.…”

Section: Discussionmentioning

confidence: 99%

Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated Tropospheric Water Vapor Content

et al. 2022

View full text Add to dashboard Cite

The assimilation of observations in limited area models (LAMs) allows to find the best possible estimate of a region’s meteorological state. Water vapor is a crucial constituent in terms of cloud and precipitation formation. Its highly variable nature in space and time is often insufficiently represented in models. This study investigates the improvement of simulated water vapor content within the Weather Research and Forecasting model (WRF) in every season by assimilating temperature, relative humidity, and surface pressure obtained from climate stations, as well as geodetically derived Zenith Total Delay (ZTD) and precipitable water vapor (PWV) data from global navigation satellite system (GNSS) ground stations. In four case studies we analyze the results of high-resolution convection-resolving WRF simulations (2.1 km) between 2016 and 2018 each in every season for a 650 × 670 km domain in the tri-border-area Germany, France and Switzerland. The impact of 3D VAR assimilation of different variables and combinations thereof, background error option, as well as the temporal and spatial resolution of assimilation is evaluated. Both column values and profiles derived from radiosondes are addressed. Best outcome was achieved when assimilating ZTD and synoptic data at an hourly resolution and a spatial thinning distance of 10 km. It is concluded that the careful selection of assimilation options can additionally improve simulation results in every season. Clear effects of assimilation on the water budgets can also be seen.

show abstract

Section: Discussionmentioning

confidence: 99%

Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated Tropospheric Water Vapor Content

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The

{k}_{1}

{k}_{2}

, and

{k}_{3}

are refractivity constants following E. Smith and Weintraub (1953): k 1 = 77.6 K hPa −1 , k 2 = 77.6 K hPa −1 , and k 3 = 3.73 × 10 5 K hPa −1 (Culverwell et al., 2015). Further reading can be found for variations of the constants (Bevis et al., 1992; Rueger, 2002) and assessment of different refractivity constants (Cucurull, 2010; Healy, 2009; Singh et al., 2019, 2021) to compute refractivity. Background model temperature, specific humidity, pressure, and geopotential height are used in the above formula.…”

Section: Methodsmentioning

confidence: 99%

Assimilation of GNSS Zenith Total Delay in NAVGEM

Christophersen¹,

Ruston

Baker³

2023

JGR Atmospheres

View full text Add to dashboard Cite

A new data source from ground‐based stations that track global navigation satellite system (GNSS) transmitters has been implemented in the Navy Global Environmental Model (NAVGEM) with the NRL Atmospheric Variational Data Assimilation System‐Accelerated Representer. The observable is the ground‐based zenith total delay (ZTD) at each ground station. This ground‐based GNSS ZTD represents the tropospheric delay of the propagation of GNSS radio L‐band signal between the transmitters and the ground stations after correcting for ionospheric effects. Here, we present the implementation procedure, quality control, single observation test, bias correction scheme, and data impact assessment. One of the critical elements in the quality control scheme is to correct for the difference between the observing ground station elevation and the numerical model terrain. A single observation test shows comparable impact from this new data source to a single radiosonde observation at a single level. The ZTD biases estimated from a 6‐month experiment run are generally small, but have a dependency on the processing center and are computed separately for each station. The forecast sensitivity to observation impact diagnosis from a 3‐month experiment demonstrates that a single ZTD observation shows similar impact as the average impact from a single tangent point of a GNSS Radio Occultation profile. The assimilation of ZTD observations also significantly improves the forecast skill by 0.25%–0.75% for wind, temperature, and precipitable water and by 1%–2% for geopotential height in the Southern Hemisphere beyond 3 days.

show abstract

“…Singh et al [35] evaluated the impact of assimilating FS7 RO refractivity profiles on the Weather Research and Forecasting (WRF) model short-range forecasts over the Indian region. The WRF model employed the three-dimensional variational assimilation (3DVar) system, and the experiment period was during the summer monsoon season (June to September 2020).…”

Section: Anthes Et Al (2021)mentioning

confidence: 99%

“…After FS7 was launched, the data quality was verified by comparing it with other independent data, such as radiosondes, model forecasts, and reanalyses [27][28][29][30][31][32][33][34][35][36][37][38]. As the first summary of the initial FS7 quality assessment, Schreiner et al (2020) [27] confirmed that FS7 RO data meet expectations of high accuracy and precision.…”

Section: Introductionmentioning

confidence: 99%

Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model

Chen¹,

Hong²,

Chen³

2022

Atmosphere

View full text Add to dashboard Cite

As the successor of FORMOSAT-3/COSMIC (FS3/C1), FORMOSAT-7/COSMIC-2 (FS7/C2) was successfully launched on 25 June 2019. FS3 radio occultation (RO) data has contributed greatly to Taiwan’s meteorological progress, improving model representations of marine boundary layer heights, cyclogenesis, tropical cyclones/typhoons, and Mei-Yu front systems. The operational CWBWRF numerical weather prediction model with the 3DEnVar data assimilating system in the Taiwan Central Weather Bureau (CWB) was adopted to evaluate the impact of assimilating FS7 RO data. The following two experiments were conducted: one assimilated the in-situ observations as in the CWB operational task (nRO), and the other additionally assimilated FS7 RO refractivity profiles (wRO). Both experiments utilized 6-h assimilating window and full cycle data assimilation strategy and made 120-h forecasts after each assimilation. Within over 70 synoptic verification cases, the biases of geopotential height, temperature, and wind were reduced in the upper model levels in wRO results, and the typhoon track and intensity prediction error reductions were statistically significant. In addition, the wRO experiment improved the typhoon structure in the initial conditions and led to a better typhoon structure forecast. These results showed that the FS7 RO refractivity assimilation could improve model forecast performance, leading to its operational use in the CWB.

show abstract

Evaluation and Assimilation of the COSMIC‐2 Radio Occultation Constellation Observed Atmospheric Refractivity in the WRF Data Assimilation System

Cited by 9 publications

References 72 publications

Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated Tropospheric Water Vapor Content

Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated Tropospheric Water Vapor Content

Assimilation of GNSS Zenith Total Delay in NAVGEM

Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model

Contact Info

Product

Resources

About