F. Joseph Turk

Evaluating and Extending the Ocean Wind Climate Data Record

Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 σo measurements include 1) direct Ku-band σo intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.

An examination of methods for estimating land surface microwave emissivity

Land surface emissivity is a critical variable for the passive microwave-based remote sensing of the land and atmosphere. Driven by the Global Precipitation Measurement mission, we implemented and evaluated a variety of approaches for quantitative estimation of land surface emissivity and its variability, within a well-defined common framework. These approaches fall into three classes: physical modeling, statistical modeling, and a hybrid of physical and statistical modeling. Every approach is subject to evaluation against retrieved emissivity over a large area in the Southern Great Plains for a period of 2 years. Physical modeling, based on two radiative transfer models coupled to a land surface modeling framework, produced reasonable estimates, with channel- and polarization-dependent errors. Calibration of these models with historical data substantially improved their performance at lower frequencies. The statistical method was tested with five different regression models, and each of them consistently outperformed physical models by about 50%. The best statistical model had an average error of 0.9–2.1%. These statistical models were slightly improved when empirical orthogonal function analysis was incorporated in the emissivity data. The hybrid approach produced errors between the uncalibrated and calibrated physical model errors. In addition to their predictive performance, other aspects of each approachs strengths and weaknesses are discussed.

Infrared satellite precipitation estimate using waveletbased cloud classification and radar calibration

We have developed a methodology to enhance an infrared-based high resolution rainfall retrieval algorithm by intelligently calibrating the rainfall estimates using space-based observations. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that using wavelet features along with other features increase the performance of rainfall estimate in terms of quantitative rain/no rain area estimates. In addition, using lightning data as a feature improves the estimates as well.

WRF nested large‐eddy simulations of deep convection during SEAC4RS

Large-eddy simulations (LES) and observations are often combined to increase our understanding and improve the simulation of deep convection. This study evaluates a nested LES method that uses the Weather Research and Forecasting (WRF) model and, specifically, tests whether the nested LES approach is useful for studying deep convection during a real-world case. The method was applied on 2 September 2013, a day of continental convection that occurred during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. Mesoscale WRF output (1.35 km grid length) was used to drive a nested LES with 450 m grid spacing, which then drove a 150 m domain. Results reveal that the 450 m nested LES reasonably simulates observed reflectivity distributions and aircraft-observed in-cloud vertical velocities during the study period. However, when examining convective updrafts, reducing the grid spacing to 150 m worsened results. We find that the simulated updrafts in the 150 m run become too diluted by entrainment, thereby generating updrafts that are weaker than observed. Lastly, the 450 m simulation is combined with observations to study the processes forcing strong midlevel cloud/updraft edge downdrafts that were observed on 2 September. Results suggest that these strong downdrafts are forced by evaporative cooling due to mixing and by perturbation pressure forces acting to restore mass continuity around neighboring updrafts. We conclude that the WRF nested LES approach, with further development and evaluation, could potentially provide an effective method for studying deep convection in real-world cases.

Introduction to the Special Issue on the Chinese FengYun-3 Satellite Instrument Calibration and Applications

The 15 papers in this special issue focus on the Chinese Fengyun (FY)-3 Satellite instrument calibration and applications.