SMAP L-Band Passive Microwave Observations Of Ocean Surface Wind During Severe Storms

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. Thesurface winds of Joaquin derived from the real-time analysis of the Center of Advanced Data Assimilation and Predictability Techniques in the Penn State University support the linearextrapolation of the Aquarius and SMAP Geophysical Model Functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface windvectors from the SMAP radiometer data to take advantage of SMAP’s two-look geometry. The SMAP radiometer wind speeds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square-difference (RMSD) with WindSat or SSMIS is 1.7 m/s below 20 m/s wind speeds. The RMSD with the ECMWF direction is 18 degrees for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with 60 km resolution and the best track peak winds estimated by the National Hurricane Center and Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8 to 18 percent on average, which is likely due to the effects of spatial averaging. There is also a very good agreement with the airborne Stepped Frequency Radiometer (SFMR) wind speeds with an average RMSD of 4.6 m/s for wind speeds in the range of 20 to 40 m/s

Predictability and Dynamics of Hurricane Joaquin (2015) Explored through Convection-Permitting Ensemble Sensitivity Experiments

Real-time ensemble forecasts from the Pennsylvania State University (PSU) WRF EnKF system (APSU) for Hurricane Joaquin (2015) are examined in this study. The ensemble forecasts, from early in Joaquin's life cycle, displayed large track spread, with nearly half of the ensemble members tracking Joaquin toward the U.S. East Coast and the other half tracking Joaquin out to sea. The ensemble forecasts also displayed large intensity spread, with many of the members developing into major hurricanes and other ensemble members not intensifying at all. Initial condition differences from the regions greater than (less than) 300 km were isolated by effectively removing initial condition differences in desired regions through relaxing each ensemble member to GFS (APSU) initial conditions. The regions of initial condition errors contributing to the track spread were examined, and the dominant source of track errors arose from the region greater than 300 km from the tropical cyclone center. Further examination of the track divergence revealed that the region between 600 and 900 km from the initial position of Joaquin was found to be the largest source of initial condition errors that contributed to this divergence. Small differences in the low-level steering flow, originating from perturbations between 600 and 900 km from the initial position, appear to have resulted in the bifurcation of the forecast tracks of Joaquin. The initial condition errors north of the initial position of Joaquin were also shown to contribute most significantly to the track divergence. The region inside of 300 km, specifically, the initial intensity of Joaquin, was the dominant source of initial condition errors contributing to the intensity spread.United States. National Aeronautics and Space Administration (Grant NNX15AM84G)United States. Office of Naval Research (Grant N000141512298)United States. Office of Naval Research (Grant N000141410062

Impacts of meteorological uncertainties on ozone pollution predictability estimated through meteorological and photochemical ensemble forecasts

[1] This study explores the sensitivity of ozone predictions from photochemical grid point simulations to small meteorological initial perturbations that are realistic in structure and evolution. Through both meteorological and photochemical ensemble forecasts with the Penn State/NCAR mesoscale model MM5 and the EPA Community Multiscale Air Quality (CMAQ) Model-3, the 24-hour ensemble mean of meteorological conditions and the ozone concentrations compared fairly well against the observations for a highozone event that occurred on 30 August during the Texas Air Quality Study of 2000 (TexAQS2000). Moreover, it was also found that there were dramatic uncertainties in the ozone prediction in Houston and surrounding areas due to initial meteorological uncertainties for this event. The high uncertainties in the ozone prediction in Houston and surrounding areas due to small initial wind and temperature uncertainties clearly demonstrated the importance of accurate representation of meteorological conditions for the Houston ozone prediction and the need for probabilistic evaluation and forecasting for air pollution, especially those supported by regulating agencies