2015: Evaluating environmental impacts on tropical cyclone rapid intensification AMERICAN METEOROLOGICAL SOCIETY predictability utilizing statistical models. Wea. Forecasting.

Most environmental satellite radiometers use solar reflectance information when it is available during the day but must resort at night to emission signals from infrared bands, which offer poor sensitivity to low-level clouds and surface features. A few sensors can take advantage of moonlight, but the inconsistent availability of the lunar source limits measurement utility. Here we show that the Day/Night Band (DNB) low-light visible sensor on the recently launched Suomi National Polar-orbiting Partnership (NPP) satellite has the unique ability to image cloud and surface features by way of reflected airglow, starlight, and zodiacal light illumination. Examples collected during new moon reveal not only meteorological and surface features, but also the direct emission of airglow structures in the mesosphere, including expansive regions of diffuse glow and wave patterns forced by tropospheric convection. The ability to leverage diffuse illumination sources for nocturnal environmental sensing applications extends the advantages of visible-light information to moonless nights.

New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geostationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene-type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool.

Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location. 1 • Introduction Passive microwave digital imagery has been proven to significantly aid the global tropical cyclone reconnaissance effort as demonstrated by products developed by the Naval Research Laboratory's (NRL) Marine Meteorology Division. Near-real-time passive microwave (PMW) imagery from both the Special Sensor Microwave/Imager (SSM/I) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) can be made available within 1-3 h for tropical cyclones worldwide. These storm-centered, high-resolution images readily display evolving storm organization and structure that are not always evident in coincident visible and infrared (vis/IR) imagery.

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat's nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or ''curtain,'' of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.