Gerald Manipon

Cloud-State-Dependent Sampling in AIRS Observations Based onCloudSatCloud Classification

AbstractThe precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within ±1 K in T and from −1 to +0.5 g kg−1 in q. Biases because of cloud-state-dependent sampling dominate the...

Rapid Damage Mapping for the 2015 Mw 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from COSMO–SkyMed and ALOS-2 Satellites

The 25 April 2015 M_w 7.8 Gorkha earthquake caused more than 8000 fatalities and widespread building damage in central Nepal. The Italian Space Agency’s COSMO–SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area four days after the earthquake and the Japan Aerospace Exploration Agency’s Advanced Land Observing Satellite-2 SAR satellite for larger area nine days after the mainshock. We used these radar observations and rapidly produced damage proxy maps (DPMs) derived from temporal changes in Interferometric SAR coherence. Our DPMs were qualitatively validated through comparison with independent damage analyses by the National Geospatial-Intelligence Agency and the United Nations Institute for Training and Research’s United Nations Operational Satellite Applications Programme, and based on our own visual inspection of DigitalGlobe’s WorldView optical pre- versus postevent imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

Cloud‐induced uncertainties in AIRS and ECMWF temperature and specific humidity

The uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching −30% at 850u2009hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud-cleared areas or above clouds within the field of regard. EC has small moist biases (~5–10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within ±1u2009K at altitudes above the 700u2009hPa level but increase with decreasing altitude. Cloud-cleared retrievals lead to large AIRS cold biases (reaching about −2u2009K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root-mean-squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5–2.5u2009K) but slightly larger than the NNW errors for thick COD (reaching 3.5u2009K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.

Recent rapid disaster response products derived from COSMO-Skymed synthetic aperture radar data

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agencys (ASIs) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agencys (JAXAs) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITARs (United Nations Institute for Training and Researchs) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobes WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

Improving the understanding of provenance and reproducibility of a multi-sensor merged climate data record

Multi-decadal climate data records are critical to studying climate variability and change. These often also require merging data from multiple instruments such as those from NASAs A-Train that contain measurements covering a wide range of atmospheric conditions and phenomena. Multi-decadal climate data record of water vapor measurements from sensors on A-Train, operational weather, and other satellites are being assembled from existing data sources, or produced from well-established methods published in peer-reviewed literature. However, the immense volume and inhomogeneity of data often requires an exploratory computing approach to product generation where data is processed in a variety of different ways with varying algorithms, parameters, and code changes until an acceptable product is generated. Furthermore, the data product information associated with source data, processing methods, parameters used, intermediate & final product outputs, and associated materials are often hidden in each of the trials and scattered throughout the processing system(s). We will present methods to help users better capture and explore the production legacy of the data, metadata, ancillary files, code, and computing environment changes used during the production of these merged and multi-sensor data products. By building provenance services on semantic and provenance technologies, we show how to leverage provenance-as-a-service to capture sufficient information to enable users to track processing, perform faceted searches on the provenance record, and visualize the provenance of the products and processing lineage. We will also present services for capturing sufficient provenance information and the associated artifacts to enable some reproducibility of these climate data records.