Application of SeaWinds scatterometer and TMI-SSM/I rain rates to hurricane analysis and forecasting (original) (raw)
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Monthly Weather Review, 2019
The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and...
Application of SSM/I satellite data to a hurricane simulation
SUMMARY The impact of Special Sensor Microwave/Imager (SSM/I) data on simulations of hurricane Danny is assessed. The assimilation of SSM/I data is found to increase the atmospheric moisture content over the Gulf of Mexico, strengthen the low-level cyclonic circulation, shorten the model spin-up time, and significantly improve the simulation of the storm's intensity. Two different approaches for assimilating SSM/I data, namely assimilating retrieved products and assimilating raw measurements, are further compared. The data-assimilation analyses from these two approaches give different moisture distributions in both the horizontal and vertical directions in the storm's vicinity, which may potentially affect the simulated storm's development; however, the simulated storm intensities are considered comparable for the Danny case. From sensitivity tests performed in this study, it is also found that the choice of the observational error variances could be potentially important to the model simulations.
Impact of the Advanced Microwave Sounding Unit Measurements on Hurricane Prediction
Monthly Weather Review, 2002
Due to the lack of meteorological observations over the tropical oceans, almost all the current hurricane models require bogusing of a vortex into the large-scale analysis of the model initial state. In this study, an algorithm to construct hurricane vortices is developed using the Advanced Microwave Sounding Unit (AMSU-A) data. Under rain-free atmospheric conditions, the temperature profile could be retrieved with a root-meansquare error of 1.5ЊC. Under heavy rainfall conditions, measurements from channels 3-5 are removed in retrieving temperatures. An application of this algorithm to Hurricane Bonnie (1998) shows well the warm-core eye and strong thermal gradients across the eyewall. The rotational and divergent winds are obtained by solving the nonlinear balance and omega equations using the large-scale analysis as the lateral boundary conditions. In doing so, the sea level pressure distribution is empirically specified, and the geopotential heights are calculated from the retrieved temperatures using the hydrostatic equation. The so-derived temperature and wind fields associated with Bonnie compare favorably to the dropsonde observations taken in the vicinity of the storm. The initial moisture field is specified based on the AMSU-derived total precipitable water. The effectiveness of using the retrieved hurricane vortex as the model initial conditions is tested using three 48-h simulations of Bonnie with the finest grid size of the 4-km, triply nested version of the fifthgeneration Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). It is found that the control run captures reasonably well the track and rapid deepening stage of the storm. The simulated radar reflectivity exhibits highly asymmetric structures of the eyewall and cloud bands, similar to the observed. A sensitivity simulation is conducted, in which an axisymmetric vortex is used in the model initial conditions. The simulated features are less favorable compared to the observations. Without the incorporation of the AMSU data, the simulated intensity and cloud structures differ markedly from the observed. The results suggest that this algorithm could provide an objective, observation-based way to incorporate a dynamically consistent vortex with reasonable asymmetries into the initial conditions of hurricane models. This algorithm could also be utilized to estimate three-dimensional hurricane flows after the hurricane warm core and eyewall are developed.
Improved hurricane wind speed algorithm for the seawinds satellite scatterometer
Oceans '02 MTS/IEEE
Abszracl-Satellite microwave scatterometer wind retrievals, given in the standard product (e.g., QuikSCAT LZB), badly underestimate the peak wind speed in tropical cyclones. One important reason is that the effects of precipitation on the normalized radar cross section sigma-0 are neglected in the processing algorithms. This paper presents results of a first attempt to provide sigma-0 corrections, which account for the atmospheric attenuation of the rain. Atmospheric transmissivity is derived from the QuikSCAT Radiometer (Q W) excess brightness temperatures taken simultaneously with sigma-0 measurements. When applied, retrieved wind speeds show improved agreement with numerical hurricane models (PSUINCAR MMS) where there is moderate to high rainfall.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spaceborne microwave sensors, measuring co-/cross-polarization (VV/VH) normalized radar cross section signals, have been widely used for tropical cyclone (TC) monitoring. However, considerable gaps remain to obtain TC intensity since these satellite data either blur inner-core structures (e.g., scatterometer data) or have limited spatial-temporal coverage (e.g., synthetic aperture radar (SAR) data). This study aims to get more accurate TC intensity estimates from scatterometers, which have good global coverage but relatively low spatial resolution. To overcome the blurring effect in scatterometers, we propose a new technique for guidance on TC intensities, with maximum 1-min sustained winds calculated as a function of decay parameters provided by the parametric Rankine-type model. The technique is employed on advanced scatterometer (ASCAT) data acquired between 2016 and 2017, validated with simultaneous SAR VH geophysical model function measurements and best-track (BT) estimates. When validated with BT estimates, the method enhances the blurred maximum winds, where the standard deviation of difference decreased from 6.3 to 3.49 m/s and the coefficient of determination increased from 0.7 to 0.89. Besides, it is noteworthy that the proposed technique performs slightly better than the Mayers-Ruf method. The promising results indicate that the technique can provide more representative TC maximum 1-min sustained wind estimates from ASCAT data, thus contributing to the further exploitation of scatterometer data for TC warnings.
Monthly Weather Review, 2015
The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first-and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions. Analysis-forecast experiments are carried out with three types of data-Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT-that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.
Four-Dimensional Variational Assimilation of Precipitation Data
Monthly Weather Review, 1995
This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rainrate estimates from the TRMM Microwave Imager. Two assimilation experiments making use of 1DVAR TCWV were run for a 15-day period. The ''Rain-1'' experiment only assimilates 1DVAR retrievals where the observed rain rate is nonzero while the ''Rain-2'' experiment assimilates all 1DVAR TCWV estimates. The period selected includes Hurricane Bonnie, which was well sampled by TRMM (late August 1998). Results show a positive impact on the humidity analysis of assimilating 1DVAR TCWV in 4DVAR. The model rain rates at the analysis time are closer to the TRMM observations showing a posteriori the consistency of the two-step approach chosen to assimilate rain-rate information in 4DVAR. The modification of the humidity analysis induces changes in the wind and pressure analysis. In particular the analysis of the track of Hurricane Bonnie is noticeably improved for the early stage of the storm development for both the Rain-1 and Rain-2 experiments. When Bonnie is in a mature stage the influence of the 1DVAR TCWV assimilation is to intensify the hurricane. Comparison with Clouds and the Earth's Radiant Energy System (CERES) measurements also show a neutral impact on the radiative fluxes at the top-of-the atmosphere when using 1DVAR TCWV estimates. The impact on the forecasts is a slight reduction of the model precipitation spindown over tropical oceans. Objective scores for the Tropics are improved, particularly for wind and for upper-tropospheric temperature. Analysis and forecast results are generally better for the Rain-2 experiment compared to Rain-1, implying that the 1DVAR TCWV estimates retrieved where no rain is observed provide useful information to 4DVAR.
Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts
In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three-dimensional variational data assimilation system (3DVAR). In particular , we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for ''control'' and ''radiance'' assimilation cases for the hurricane Sandy period. In ''control'' assimilation experiment, only conventional air and surface observations data are assimilated, while, in ''radiance'' assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short-to medium-range forecast (up to *60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation.
Satellite Microwave Surface Observations in Tropical Cyclones
Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/ visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.
Impact of Oceansat-2 Scatterometer Winds and TMI Observations on Phet Cyclone Simulation
IEEE Transactions on Geoscience and Remote Sensing, 2000
The Indian Space Research Organisation launched the Oceansat-2 scatterometer (OSCAT) for atmospheric and oceanographic applications. In this paper, a case study has been performed to assess the impact of OSCAT-retrieved wind vectors on the simulation of tropical cyclone Phet over the Arabian Sea. Three-dimensional variational data assimilation of the Weather Research and Forecasting model is used for this purpose. In addition to OSCAT winds, wind speed and precipitable water derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are also used for assimilation to evaluate the impact of scatterometer and radiometer data on tropical cyclone prediction. Results show that an ∼60-km track error is observed in control and TMI experiments when compared with Joint Typhoon Warning Center observed cyclone center at 1800 UTC 01 June 2010. An approximately 40-km track error is determined in the initial center position of OSCAT experiments. The mean track error forecast is less in OSCAT experiments (∼80 km) in comparison with TMI experiments (∼110 km). Only OSCAT data experiments are able to predict the track of the cyclone toward the Oman coast. Assimilation of scatterometer wind direction improves the track forecast; but it degrades the forecast of the intensity, maximum magnitude, and evolution of the cyclone. None of these experiments are able to capture the observed minimum sea level pressure (964 hPa at 1200 UTC 02 June 2010) accurately. TMI experiments are slightly better than OSCAT experiments in capturing the intensity of cyclone Phet, whereas wind direction from OSCAT improves the track forecast of the cyclone. Index Terms-Assimilation, ocean surface winds, Oceansat-2, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Weather Research and Forecasting (WRF) model.