Zhengkun Qin

Evaluating Added Benefits of Assimilating GOES Imager Radiance Data in GSI for Coastal QPFs

AbstractThe Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any o...

Improved Coastal Precipitation Forecasts with Direct Assimilation of GOES-11/12 Imager Radiances

AbstractThe Geostationary Operational Environmental Satellite (GOES) imager provides observations that are of high spatial and temporal resolution and can be applied for effectively monitoring and nowcasting severe weather events. In this study, improved quantitative precipitation forecasts (QPFs) for three coastal storms over the northern Gulf of Mexico and the East Coast is demonstrated by assimilating GOES-11 and GOES-12 imager radiances into the Weather Research and Forecasting (WRF) model. Both the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to ingest GOES IR clear-sky data. Assimilation of GOES imager radiances during a 6–12-h time window prior to convective initiation and/or development could significantly improve the precipitation forecasts near the coast of the northern Gulf of Mexico. The 3-h accumulative precipitation threat scores are increased by about 20% after 6 ...

Improved Tropical Storm Forecasts withGOES-13/15Imager Radiance Assimilation and Asymmetric Vortex Initialization in HWRF

AbstractThe Geostationary Operational Environmental Satellite (GOES) imagers provide high temporal- and spatial-resolution data for many applications, such as monitoring severe weather events. In this study, radiance observations of four infrared channels from GOES-13 and GOES-15 imagers are directly assimilated using the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system to produce the initial conditions for the Hurricane Weather Research and Forecasting Model (HWRF). Impacts of GOES imager data assimilation on track and intensity forecasts are demonstrated for a landfalling tropical storm that moved across the Gulf of Mexico—Debby (2012). With a higher model top and a warm start, an asymmetric component is also added to the original HWRF symmetric vortex initialization. Two pairs of data assimilation and forecasting experiments are carried out for assessing the impacts of the GOES imager data assimilation on tropical storm forecasts. The first ...

Improved Quantitative Precipitation Forecasts by MHS Radiance Data Assimilation with a Newly Added Cloud Detection Algorithm

AbstractSatellite microwave humidity sounding data are assimilated through the gridpoint statistical interpolation (GSI) analysis system into the Advanced Research core of the Weather Research and Forecasting (WRF) model (ARW) for a coastal precipitation event. A detailed analysis shows that uses of Microwave Humidity Sounder (MHS) data from both NOAA-18 and MetOp-A results in GSI degraded precipitation threat scores in a 24-h model forecast. The root cause for this degradation is related to the MHS quality control algorithm, which is supposed to remove cloudy radiances. Currently, the GSI cloud detection is based on the brightness temperature differences between observations and the model background state at two MHS window channels. It is found that the GSI quality control algorithm fails to identify some MHS cloudy radiances in cloud edges where the ARW model has no cloud and the water vapor amount is low. A new MHS cloud detection algorithm is developed based on a statistical relationship between three...

Detection of Radio-Frequency Interference Signal Over Land From FY-3B Microwave Radiation Imager (MWRI)

The MicroWave Radiation Imager (MWRI) onboard the FengYun (FY)-3B satellite has five frequencies at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, each having dual channels at vertical and horizontal polarization states, respectively. It is found that radio-frequency interference (RFI) is present in MWRI data over land. The RFI signals are, in general, detectable from a spectral difference method and a principal component analysis (PCA) method. In particular, the PCA method is applied to derive RFI signals from natural radiations by using the characteristics of natural radiation measurements having all-channel correlations. In the area where data have a higher projection onto the first principle component (PC) mode, RFI is, in general, present. However, both the spectral and PCA methods cannot detect RFI reliably over frozen grounds and scattering surfaces, where the brightness temperature difference between 10.65 and 18.7 GHz is large. Thus, detection is improved through the use of normalized PCA. The new RFI detection algorithm is now working reliably for MWRI applications. It is found that RFI at 10.65 GHz distributes widely over Europe and Japan, and is less popular over the United States and China.

Fengyun-3B MicroWave Humidity Sounder (MWHS) Data Noise Characterization and Filtering Using Principle Component Analysis

MicroWave Humidity Sounder (MWHS) onboard both Fengyun-3A (FY-3A) and FY-3B satellites have three channels (channels 3-5) near the 183-GHz water-vapor absorption line. These channel frequencies are also used in other instruments such as Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) onboard MetOp and NOAA satellites. Both MWHS and MHS are cross-track scanners. In this paper, a comparison between the simulated brightness temperatures with MWHS measurements clearly shows that MWHS observations from the three sounding channels contain a scan-angle-dependent cohesive noise along the instrument scanline. This noise does not cancel out when a large amount of data over a sufficiently long period of time is averaged, which eliminates the possibility of such a noise to arise from the natural variability of the atmosphere and the surface. The noises are around 0.3, 0.2, and 0.2 K for channels 3-5, respectively. A principle component analysis is used for the characterization of this cohesive noise using one-month FY-3B MWHS data. It is shown that the MWHS cohesive noise is primarily contained in the first principal component (PC) mode, which mainly describes a scan-angle-dependent brightness temperature variation, i.e., a unique feature of the cross-tracking instrument. The first PC accounts for more than 99.91 % total variance in the three MWHS sounding channels. A five-point smoother is then applied to the first PC, which effectively removes such a data noise in the MWHS data. The reconstruction of the MWHS radiance spectra using the noise-filtered first PC component is of good quality. The scan-angle-dependent bias from the reconstructed MWHS data becomes more uniform and is consistent with the NOAA-18 MHS data.

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

Abstract The launch of the Japanese Advanced Himawari Imager (AHI) on 7 October 2014 represents a new era of geostationary operational environmental satellite (GOES) imagers, providing many more channels than any previously launched GOES imagers for the first time. In this study, we compare the impacts of assimilating all AHI versus GOES-like infrared channels radiances on regional forecasts over Eastern China. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and Advanced Research Weather Research and Forecast model are employed. Positive impacts are obtained on quantitative precipitation forecasts (QPFs) associated with a typical summer precipitation case over eastern China in both set-ups, i.e. one assimilating all 10 AHI infrared channels (AHIA) and the other assimilating only four GOES-like AHI channels (AHIG). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward-propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHIA experiment generated the largest improvement on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHIG experiment and a control experiment without assimilating any AHI channel.

Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops

The Advanced Microwave Sounding Unit-A (AMSU-A) onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A, the hyperspectral Atmospheric Infrared Sounder (AIRS) onboard Aqua, the High resolution InfraRed Sounder (HIRS) onboard NOAA-19 and MetOp-A, and the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting (HWRF) system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

Time Zone Dependence of Diurnal Cycle Errors in Surface Temperature Analyses

Abstract Surface temperatures from both the NCEP analysis and ECMWF Re-Analysis (ERA-Interim) in January 2008 over the Africa–Eurasian region were compared with surface station measurements to study analysis errors in the diurnal cycle, with data sampled at 3-h time intervals. The results show the dominance of the diurnal cycle in surface temperature analyses and the significance of the time zone dependence of diurnal cycle errors in global analyses. While a distinct diurnal cycle of surface temperature is evidenced in all three datasets, with a nearly constant thermal response of about 2 h in surface station data, the thermal response in the two global analyses varies with longitude. A smaller error in analysis is found in time zones where the observed maximum temperatures (Tmax) occurred close to a global analysis time (e.g., 0000, 0600, 1200, or 1800 UTC). The thermal response errors in surface temperature analyses increase linearly with the difference between the observed diurnal peak time and the ana...

Development and Initial Assessment of a New Land Index for Microwave Humidity Sounder Cloud Detection

This paper describes a new quality control (QC) scheme for microwave humidity sounder (MHS) data assimilation. It consists of a cloud detection step and an O–B (i.e., differences of brightness temperatures between observations and model simulations) check. Over ocean, cloud detection can be carried out based on two MHS window channels and two Advanced Microwave Sounding Unit-A (AMSU-A) window channels, which can be used for obtaining cloud ice water path (IWP) and liquid water path (LWP), respectively. Over land, cloud detection of microwave data becomes much more challenging due to a much larger emission contribution from land surface than that from cloud. The current MHS cloud detection over land employs an O–B based method, which could fail to identify cloudy radiances when there is mismatch between actual clouds and model clouds. In this study, a new MHS observation based index is developed for identifying MHS cloudy radiances over land. The new land index for cloud detection exploits the large variability of brightness temperature observations among MHS channels over different clouds. It is shown that those MHS cloudy radiances that were otherwise missed by the current O–B based QC method can be successfully identified by the new land index. An O–B check can then be employed to the remaining data after cloud detection to remove additional outliers with model simulations deviated greatly from observations. It is shown that MHS channel correlations are significantly reduced by the newly proposed QC scheme.