Impact of MODIS and AIRS total precipitable water on modifying the vertical shear and Hurricane Emily simulations

Abstract

[1] The impact of retrieved total precipitable water (TPW) from Moderate Resolution Imaging Spectrometer (MODIS) infrared (IR), MODIS near-infrared (NIR), and the combined Atmospheric Infrared Sounder (AIRS)-IR and Advanced Microwave Sounding Unit-Microwave channels on simulations of Hurricane Emily was assessed and compared using the Weather Research and Forecasting model and its three-dimensional variation data assimilation (3D-Var) system. After assimilating MODIS IR TPW, the model clearly better reproduced storm tracking, intensity, and the 10 m wind field, while the improvement was limited or nil when assimilating either MODIS NIR TPW or AIRS TPW. After the data assimilation of MODIS IR TPW, a positive moisture increment was present to the east of the simulated storm in 3D-Var analysis (i.e., initial conditions). The positive TPW increment enhanced a convective cloud, which was also observed by satellites. The convective cloud effectively modulated the height and wind fields, resulting in a weakening of the vertical wind shear (VWS) over the region. The weak VWS band was then advected to the north of the storm, preventing the storm from attaching to the strong VWS zone located between 20°N and 30°N. There was no such positive moisture increment, convective cloud, or weak VWS band occurring to the east of the simulated storm in the other data assimilation experiments. This explains why the simulated storm intensified with assimilation of MODIS IR TPW but not for the other experiments.

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

“…While the GFS forecast TPW is found to be of good quality relative to the truth datasets (i.e., low RMSE, STD, and BIAS errors and high correlation coefficients), the GOES-13 LAP TPW retrievals are found to be superior in most aspects, indicating that the GOES-13 sounder does indeed provide additional moisture information that can improve weather forecasts using current nowcasting and assimilation techniques (e.g., Chen et al 2008;Liu et al 2011). …”

“…The STD and BIAS profiles of the GOES-13 LAP (a) temperature and (b) relative humidity, and the GFS forecast (c) temperature and (d) relative humidity compared to the ARM CART (top) raob (sample size is 1079) and the (bottom) conventional raob (sample size is 25 529). showed that the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) TPW data into the Weather Research and Forecasting (WRF) model slightly improved simulated rainfall over the region of interest in southern Oklahoma for a severe thunderstorm case in June 2004, and the hurricane track or intensity was improved by the use of MODIS TPW data for Hurricane Isadore over the ocean in September 2002 Liu et al (2011). It is one of the critical variables used by forecasters in the weather forecasting discussions at the National Weather Service's Weather Prediction Center when there are expectations of flash flooding or severe weather.…”

Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event

“…Recent studies have used advanced assimilation methods to directly assimilate WV sensitive infrared radiances and WV profile retrievals. Li and Liu [2009] and Liu et al [2011]found that WV and temperature retrievals from the Advanced Infrared Sounder (AIRS) and the Moderate‐resolution Imaging Spectroradiometer (MODIS) reduced hurricane forecast intensity and track errors when assimilated using an ensemble Kalman filter (EnKF) [ Evensen , 1994] assimilation system. Hurricane forecasts were also improved when AIRS WV retrievals were assimilated using a 3DVAR assimilation system [ Pu and Zhang , 2010].…”

Understanding the impact of assimilating FORMOSAT‐7/COSMIC‐2 radio occultation refractivity on tropical cyclone genesis: Observing system simulation experiments using Hurricane Gordon (2006) as a case study

“…Space-borne measurements have been an important source of observations over oceans since the 1970s. However, though many satellite observations are available over oceans, the lack of high vertical resolution and the contamination of data by clouds or heavy rainfall (S.-H. Chen et al, 2008;Liu et al, 2011) can reduce the benefit of these data, particularly within and around severe weather areas, such as TCs. For example, the new generation of geostationary satellites with high spatial and temporal resolution, such as Himawari-8/9 and Geostationary Operational Environmental Satellites 16/17, have a great advantage of observing the environment for TC formation, but difficulties remain in accurately measuring the low-level atmosphere when a cluster of convective clouds emerges.…”