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Kahn, Brian

Publications and source records attributed to Kahn, Brian.

At least 19 records

Orbital Trade Study for the PREFIRE Mission

The Polar Radiant Energy in the Far-Infrared Experiment (PREFIRE) is a low-cost cubesat-based Earth Science mission selected for rapid implementation through NASAs Earth Ventures-Instrument program. Two cubesats carrying thermal infrared spectrometers (TIRS) in polar orbits will test long-held assumptions regarding radiative balance at long wavelengths where the cold-dry Arctic and Antarctic surfaces expel significant amounts of the earth's radiant energy. Thermal radiometry with better than one micron sampling at wavelengths beyond 15 microns will provide critical observational bounds for inputs into glacier melt and climate change models through improvements in surface emissivities, longwave greenhouse effect parameterizations, and cloud presence/types. Additional information on potential far-infrared surface/atmosphere feedbacks during rapid melt processes may be captured with sub-diurnal revisits of targeted areas. A baseline mission with two cubesats in different orbits provides a means for optimization of sub-diurnal sampling for science purposes. In this presentation we provide details of orbit studies that inform expected sampling metrics. Candidate orbits are modeled in SGP and then TIRS sampling is projected into surface footprints with appropriate geo-location. Matching algorithms then compile statistics for time-delayed revisits between and within the two orbits. Binning of results by latitude allows for abstraction of precession issues as well as for correlations with surface types as defined by radiometric classification schemes. Multiple acceptable pairs of science-driven orbits were found that optimize sampling in areas known to experience rapid melt events. The final choice of PREFIRE orbital parameters includes the systems trade-space, particularly the power budget, as well as the availability of launch opportunities.

"L'Ecuyer, Tristan"

An A-Train Climatology of Extratropical Cyclone Clouds

Extratropical cyclones (ETCs) are the main purveyors of precipitation in the mid-latitudes, especially in winter, and have a significant radiative impact through the clouds they generate. However, general circulation models (GCMs) have trouble representing precipitation and clouds in ETCs, and this might partly explain why current GCMs disagree on to the evolution of these systems in a warming climate. Collectively, the A-train observations of MODIS, CloudSat, CALIPSO, AIRS and AMSR-E have given us a unique perspective on ETCs: over the past 10 years these observations have allowed us to construct a climatology of clouds and precipitation associated with these storms. This has proved very useful for model evaluation as well in studies aimed at improving understanding of moist processes in these dynamically active conditions. Using the A-train observational suite and an objective cyclone and front identification algorithm we have constructed cyclone centric datasets that consist of an observation-based characterization of clouds and precipitation in ETCs and their sensitivity to large scale environments. In this presentation, we will summarize the advances in our knowledge of the climatological properties of cloud and precipitation in ETCs acquired with this unique dataset. In particular, we will present what we have learned about southern ocean ETCs, for which the A-train observations have filled a gap in this data sparse region. In addition, CloudSat and CALIPSO have for the first time provided information on the vertical distribution of clouds in ETCs and across warm and cold fronts. We will also discuss how these observations have helped identify key areas for improvement in moist processes in recent GCMs. Recently, we have begun to explore the interaction between aerosol and cloud cover in ETCs using MODIS, CloudSat and CALIPSO. We will show how aerosols are climatologically distributed within northern hemisphere ETCs, and how this relates to cloud cover.

clouds

Observation-Based Cloud Radiative Kernels from A-Train

We develop a method to empirically derive broadband and spectral cloud radiative kernels by cloud type from pixel-scale collocated A-Train observations and reanalysis, which does not require additional cloudy radiative transfer calculations nor cloud properties. This method is able to estimate the cloud feedback by maintaining the consistency between CRKs and cloud responses.

Yue, Qing

A Multi-Sensor Water Vapor, Temperature and Cloud Climate Data Record: Status of Our MEaSUREs 2012 Project

- Our primary objectives: Incorporate all A-Train water vapor, including small-scale, horizontal and vertical water vapor structure from MODIS (Moderate Resolution Imaging Spectroradiometer) and GPS (Global Positioning System); Use MODIS cloud classification that is collocated at the pixel-scale over the full AIRS/AMSU (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit) swath; Extend summaries to PDFs (Probability Distribution Functions)/higher order moments of water vapor sorted by cloud classes; Establish robust, scale-dependent statistical relationships between cloud, temperature, and water vapor PDFs for the climate modeling community. - We will also: Update our current data record to new CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification; Provide merged AIRS and MLS (Microwave Limb Sounder) water vapor profiles using averaging kernels; Include temperature and water vapor from models (ECMWF - European Centre for Medium-Range Weather Forecasts) and re-analyses (MERRA - Modern Era-Retrospective Analysis for Research and Applications); Classify METOP-A (Meteorological Operational Satellite-A) and NOAA (National Oceanic and Atmospheric Administration) satellite water vapor with AVHRR (Advanced Very High Resolution Radiometer) during SNOs (Solar Neutrino Oscillations); Compare with the NVAP-M (re-analysis and extension of the NASA Water Vapor Project (NVAP)) water vapor climatologies; Use existing data sources and mature algorithms, and document processing algorithms.

Fetzer, Eric