Community Gridpoint Statistical Interpolation Research Articles

Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018

Abstract. As a component of the National Aeronautics and Space Administration's (NASA's) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January–March 2018. In this study, the retrieved ocean surface meteorological products – 2 m temperature, 2 m specific humidity, and 10 m wind speed – were assimilated into a regional numerical weather prediction (NWP) framework. This explored the application of these observations for two heavy snowfall events during the ICE-POP 2018, on 27–28 February and 7–8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high-resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, a significantly positive influence of the retrieved surface observation was found for the March case, with improved quantitative precipitation forecasts and reduced errors in temperature forecasts. A slightly smaller yet positive impact was also found in the forecast for the February case.

Simulation of Continental Shallow Cumulus Populations Using an Observation‐Constrained Cloud‐System Resolving Model

AbstractContinental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems (HI‐SCALE) field campaign are simulated by using an observation‐constrained cloud‐system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four‐dimensional ensemble‐variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow‐to‐deep transitions of convective clouds.

Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System

The MicroWave Humidity Sounder 2 (MWHS-2) onboard the FY-3C satellite provides an extra important data source for atmospheric water vapor monitoring besides the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). This paper introduces MWHS-2 radiance data into the community Gridpoint Statistical Interpolation (GSI) global analysis system. More than one-year cycling assimilation experiments with and without MWHS-2 data are performed. Results show that MWHS-2 has similar data quality to MHS and ATMS. The biases of MWHS-2 are stable except some sudden jumps that can be removed nicely by the variational bias correction scheme within GSI. Assimilating MWHS-2 makes the 6-h forecasts fit more closely to radiosonde observations, with a reduction of 0.55–1% for the observation-minus-simulation standard deviation of specific humidity. The 500 hPa geopotential height anomaly correlation scores are increased by around 0.006 for the 144-h forecast, indicating that assimilating MWHS-2 may also help to improve 3–8-day forecasts.

Assimilation of Radial Winds Over India Using a Community GSI Analysis System

With the modernization of the India Meteorological Department, the observational network of the country, both in situ and remote sensing, is enhanced. The Doppler Weather Radar (DWR) network is improved, and the National Centre for Medium Range Weather Forecasting is acquiring radar data from 20 stations all over the country. The maximum utilization of DWR observations in the numerical models remains a challenging task. This study represents the first assessment of assimilation of radial wind observations from all 20 DWR stations, utilizing the resources of weather research and a forecasting model with a community grid-point statistical interpolation system. DWR observations are an important data source for mesoscale and microscale weather analysis and forecasting because of their high temporal and spatial resolution. However, the representation of DWR radial wind and reflectivity in a desired format seems to be crucial in the modeling approach. A series of experiments are conducted to evaluate the sensitivity of the analysis to the velocity azimuth display quality control (VADQC) and without VADQC (VARQC) to understand the effect of QC on analysis. The statistical analysis of assimilation of DWR radial wind suggests that a gate distance of 250 m or its multiple is imperative for the setup of the DWR. Additionally, the density of the super-observation is amplified in the VARQC approach. The analysis procedure is implemented for the recent severe cyclone Phethai (December 2018) over the Bay of Bengal, and a few preliminary results are discussed.

Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events

AbstractThe National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.

A Precipitation Detection Method for MWTS-II Radiance Assimilation in Typhoon Simulation

FY-3C Microwave Temperature Sounder Ⅱ (MWTS-Ⅱ) lacks observations at 23.8 GHz, 31 GHz and 89 GHz, making it difficult to remove the data contaminated by precipitation in assimilation. In this paper, a fast forward operator based on the Community Radiative Transfer Model (CRTM) was used to analyze the relationship between the observation minus background simulation (O-B) and the cloud fractions in different MWTS-Ⅱ channels. In addition, based on the community Gridpoint Statistical Interpolation (GSI) system, the radiation brightness temperature of the MWTS-Ⅱ was assimilated in the regional Numerical Weather Prediction (NWP) model. In the process of assimilation, Visible and Infrared Radiometer (VIRR) cloud detection products were matched to MWTS-Ⅱ pixels for precipitation detection. For typhoon No. 18 in 2014, impact tests of MWTS-Ⅱ data assimilation was carried out. The results show that, though the bias observation minus analysis (O-A) of assimilated data can be reduced by quality control only with | O-B | < 3K; however, the O-A becomes much smaller while the precipitation detection is performed with Fvirr < 0.9 (VIRR cloud fraction threshold of 0.9). Besides, the change of the environmental field around the typhoon is more conducive to make the simulated track closer to the observation. The 72-hour typhoon track simulation error also shows that, after the precipitation detection, the error of simulated typhoon track is significantly reduced, which reflects the validity of a precipitation detection method based on a double criterion of | O-B | < 3K and Fvirr < 0.9.

A Two-Season Impact Study of Radiative Forced Tropospheric Response to Stratospheric Initial Conditions Inferred From Satellite Radiance Assimilation

This study investigated the impacts of stratospheric temperatures and their variations on tropospheric short-term weather forecasting using the Advanced Research Weather Research and Forecasting (WRF-ARW) system with real satellite data assimilation. Satellite-borne microwave stratospheric temperature measurements up to 1 mb, from the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS), and the Special Sensor microwave Imager/Sounder (SSMI/S), were assimilated into the WRF model over the continental U.S. during winter and summer 2015 using the community Gridpoint Statistical Interpolation (GSI) system. Adjusted stratospheric temperature related to upper stratospheric ozone absorption of short-wave (SW) radiation further lead to vibration in downward SW radiation in winter predictions and overall reduced with a maximum of 5.5% reduction of downward SW radiation in summer predictions. Stratospheric signals in winter need 48- to 72-h to propagate to the lower troposphere while near-instant tropospheric response to the stratospheric initial conditions are observed in summer predictions. A schematic plot illustrated the physical processes of the coupled stratosphere and troposphere related to radiative processes. Our results suggest that the inclusion of the entire stratosphere and better representation of the upper stratosphere are important in regional NWP systems in short-term forecasts.

Assessment of the Impact of FORMOSAT‐7/COSMIC‐2 GNSS RO Observations on Midlatitude and Low‐Latitude Ionosphere Specification: Observing System Simulation Experiments Using Ensemble Square Root Filter

AbstractThe Formosa Satellite‐7/Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (FORMOSAT‐7/COSMIC‐2) Global Navigation Satellite System radio occultation (RO) payload can provide global observations of slant total electron content (sTEC) with an unprecedentedly high spatial temporal resolution. Recently, a new ionospheric data assimilation system, the Community Gridpoint Statistical Interpolation (GSI) Ionosphere, is constructed with the National Oceanic and Atmospheric Administration GSI Ensemble Square Root Filter and the Global Ionosphere Plasmasphere and the Thermosphere Ionosphere Electrodynamic General Circulation Model. The paper demonstrates the capability of the GSI Ionosphere to improve the ionospheric specification and make a quantitative assessment of the impact of FORMOSAT‐7/COSMIC‐2 RO data on the ionospheric observing system simulation experiments conducted to calibrate key Ensemble Square Root Filter parameters that control detrimental effects of the sampling errors, particularly on the ensemble‐based estimation of the correlation between observations and model states, in order to yield high‐quality assimilation analysis. Results from the observing system simulation experiments show that (1) an ensemble size larger than 70 is recommended for assimilation of RO sTEC data with the GSI Ionosphere and (2) localizing the impact of observations around the tangent points in the horizontal direction with a length scale of 5,000 km is effective in improving assimilation analysis quality. Assimilation of sTEC data from FORMOSAT‐7/COSMIC‐2 can considerably improve the global ionospheric specification through the application of the GSI Ionosphere. The GSI Ionosphere can provide instantaneous global pictures of the ionosphere variability and help characterize day‐to‐day variability of the ionosphere and deepen our understanding of the observed day‐to‐day variability.

GSI Three-Dimensional Ensemble–Variational Hybrid Data Assimilation Using a Global Ensemble for the Regional Rapid Refresh Model

The Rapid Refresh (RAP) is an hourly updated regional meteorological data assimilation/short-range model forecast system running operationally at NOAA/National Centers for Environmental Prediction (NCEP) using the community Gridpoint Statistical Interpolation analysis system (GSI). This paper documents the application of the GSI three-dimensional hybrid ensemble–variational assimilation option to the RAP high-resolution, hourly cycling system and shows the skill improvements of 1–12-h forecasts of upper-air wind, moisture, and temperature over the purely three-dimensional variational analysis system. Use of perturbation data from an independent global ensemble, the Global Data Assimilation System (GDAS), is demonstrated to be very effective for the regional RAP hybrid assimilation. In this paper, application of the GSI-hybrid assimilation for the RAP is explained. Results from sensitivity experiments are shown to define configurations for the operational RAP version 2, the ratio of static and ensemble background error covariance, and vertical and horizontal localization scales for the operational RAP version 3. Finally, a 1-week RAP experiment from a summer period was performed using a global ensemble from a winter period, suggesting that a significant component of its multivariate covariance structure from the ensemble is independent of time matching between analysis time and ensemble valid time.

Application of IVAP-Based Observation Operator in Radar Radial Velocity Assimilation: The Case of Typhoon Fitow

An improved Doppler radar radial velocity assimilation observation operator is proposed based on the integrating velocity–azimuth process (IVAP) method. This improved operator can ingest both radial wind and its spatial distribution characteristics to deduce the two components of the mean wind within a given area. With this operator, the system can be used to assimilate information from tangential wind and radial wind. On the other hand, because the improved observation operator is defined within a given area, which can be uniformly chosen in both the observation and analysis coordinate systems, it has a thinning function. The traditional observation operator and the improved observation operator, along with their corresponding data processing modules, were implemented in the community Gridpoint Statistical Interpolation analysis system (GSI) to demonstrate the superiority of the improved operator. The results of single analysis unit experiments revealed that the two operators are comparable when the analysis unit is small. When the analysis unit becomes larger, the analysis results of the improved operator are better than those of the traditional operator because the former can ingest more wind information than the latter. The results of a typhoon case study indicated that both operators effectively ingested radial wind information and produced more reasonable typhoon structures than those in the background fields. The tangential velocity relative to the radar was retrieved by the improved operator through ingesting tangential wind information from the spatial distribution characteristics of radial wind. Because of the improved vortex intensity and structure, obvious improvements were seen in both track and intensity predictions when the improved operator was used.

Development of fine‐resolution analyses and expanded large‐scale forcing properties: 2. Scale awareness and application to single‐column model experiments

AbstractFine‐resolution three‐dimensional fields have been produced using the Community Gridpoint Statistical Interpolation (GSI) data assimilation system for the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Southern Great Plains region. The GSI system is implemented in a multiscale data assimilation framework using the Weather Research and Forecasting model at a cloud‐resolving resolution of 2 km. From the fine‐resolution three‐dimensional fields, large‐scale forcing is derived explicitly at grid‐scale resolution; a subgrid‐scale dynamic component is derived separately, representing subgrid‐scale horizontal dynamic processes. Analyses show that the subgrid‐scale dynamic component is often a major component over the large‐scale forcing for grid scales larger than 200 km. The single‐column model (SCM) of the Community Atmospheric Model version 5 is used to examine the impact of the grid‐scale and subgrid‐scale dynamic components on simulated precipitation and cloud fields associated with a mesoscale convective system. It is found that grid‐scale size impacts simulated precipitation, resulting in an overestimation for grid scales of about 200 km but an underestimation for smaller grids. The subgrid‐scale dynamic component has an appreciable impact on the simulations, suggesting that grid‐scale and subgrid‐scale dynamic components should be considered in the interpretation of SCM simulations.

Development of fine‐resolution analyses and expanded large‐scale forcing properties: 1. Methodology and evaluation

AbstractWe produce fine‐resolution, three‐dimensional fields of meteorological and other variables for the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The Community Gridpoint Statistical Interpolation system is implemented in a multiscale data assimilation (MS‐DA) framework that is used within the Weather Research and Forecasting model at a cloud‐resolving resolution of 2 km. The MS‐DA algorithm uses existing reanalysis products and constrains fine‐scale atmospheric properties by assimilating high‐resolution observations. A set of experiments show that the data assimilation analysis realistically reproduces the intensity, structure, and time evolution of clouds and precipitation associated with a mesoscale convective system. Evaluations also show that the large‐scale forcing derived from the fine‐resolution analysis has an overall accuracy comparable to the existing ARM operational product. For enhanced applications, the fine‐resolution fields are used to characterize the contribution of subgrid variability to the large‐scale forcing and to derive hydrometeor forcing, which are presented in companion papers.