Comprehensive Large Array-data Stewardship System Research Articles

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder (MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path (IWP) as well as the influence of the cloud liquid water path (LWP) on the relationship is examined. Seven years (2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version 2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from −1.7 K to 1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitude-dependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels, the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.

Comparison of Atmospheric Methane Retrievals From AIRS and IASI

Atmospheric methane (CH4) is a standard product of the atmospheric infrared sounder (AIRS) aboard NASA's Aqua satellite, generated at the NASA Goddard Earth Sciences Data and Information Services Center (NASA/GES/DISC), and a product of the infrared atmospheric sounding interferometer (IASI) aboard METOP-A,-B, generated at National Oceanic and Atmospheric Administration's Comprehensive Large Array-data Stewardship System. In order to understand the capability of these two sensors in observing the spatial and temporal distribution of CH4 , this paper compares the CH4 products from AIRS and IASI with aircraft measurements, as well as the corresponding time series in tropics and high northern latitude regions. It is found that the mean degree of freedom from AIRS is smaller than IASI by –0.049 ± 0.152, and in their peak sensitive altitude between 350 and 650 hPa their difference (AIRS – IASI) is about 2.8 ± 17.2 ppb. Both AIRS and IASI can capture the latitudinal gradient, but there is a large scattering in the high northern latitude regions. They agree well in observing the summer enhancement of CH4 during the Monsoon season over South Asia, and the seasonal cycles over Siberia (except for a relatively larger difference in the cold season). These results highlight that AIRS and IASI can provide valuable information to capture the spatiotemporal variation of CH4 in the mid-upper troposphere in most periods and regions, but it is needed to further improve the data quality to make a consistent product using both sensors.

WAVEFORM IDENTIFICATION OF ALTIMETRY SATELLITE DATA OF SHALLOW AND DEEP WATERS IN SOUTHERN JAVA WATERS

ABSTRACT Waveform patterns of satellite altimetry affect the accuracy of sea surface height estimation from the satellite. The waveform patterns found in the coastal waters are generally not in the ideal form (Brown-waveform), resulting inaccurate in sea surface height estimation. The objec-tives of this research were to identify patterns of the waveform and determine their variability. Satellite altimetry Jason-2 SGDR (Sensor Geophysical Data Record) type D data located in the southern Java island waters of the year of 2013 were used and downloaded from “NOAA's Comprehensive Large Array-data Stewardship System” (www.class.ncdc.noaa.gov) . Waveform identification and analyses were conducted along the satellite pass within the distance of 0-10 km, 10-50 km, and 50-100 km form the coastline. Results showed that the highest number of non-Brown-waveform was found within 0-10 km of the coastline (69%). Meanwhile, within the distance of 10-50 km and 50-100 km from the coastline, the number of non-Brown waveform was 5% and 3%, respectively. Brown waveform patterns could be found generally starting at 7.58 km from the coastline. Factors such as land near coastal waters, the depth and shape of the surface waters, aerosols in the atmosphere, building (example: lighthouse or ship) found in coastal areas suspected to be the cause of the noise in waveforms. Keywords: Borwn and non-Brown waveform, sea level height, altimetry satellite, identification

Mid-upper tropospheric methane retrieval from IASI and its validation

Abstract. Mid-upper tropospheric methane (CH4), as an operational product at NOAA's (National Oceanic and Atmospheric Administration) Comprehensive Large Array-data Stewardship System (CLASS), has been retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) since 2008. This paper provides a description of the retrieval method and the validation using 596 CH4 vertical profiles from aircraft measurements by the HIAPER Pole-to-Pole Observations (HIPPO) program over the Pacific Ocean. The number of degrees of freedom for the CH4 retrieval is mostly less than 1.5, and it decreases under cloudy conditions. The retrievals show greatest sensitivity between 100–600 hPa in the tropics and 200–750 hPa in the mid- to high latitude. Validation is accomplished using aircraft measurements (convolved by applying the monthly mean averaging kernels) collocated with all the retrieved profiles within 200 km and on the same day, and the results show that, on average, a larger error of CH4 occurs at 300–500 hPa. The bias in the trapezoid of 374–477 hPa is −1.74% with a residual standard deviation of 1.20%, and at layer 596–753 hPa the bias is −0.69% with a residual standard deviation of 1.07%. The retrieval error is relatively larger in the high northern latitude regions and/or under cloudy conditions. The main reasons for this negative bias include the uncertainty in the spectroscopy near the methane Q branch and/or the empirical bias correction, plus residual cloud contamination in the cloud-cleared radiances. It is expected for NOAA to generate the CH4 product for 20 + years using a similar algorithm from three similar thermal infrared sensors: Atmospheric Infrared Sounder (AIRS), IASI and the Cross-track Infrared Sounder (CrIS). Such a unique product will provide a supplementary to the current ground-based observation network, particularly in the Arctic, for monitoring the CH4 cycle, its transport and trend associated with climate change.

Towards Data and Sensor Planning Service for Coupling Earth Science Models and Earth Observations

How to obtain sensor observation data easily and quickly is one of the challenging research areas in the Earth science community. Data and Sensor Planning Service aims to generate requirements on Earth observations data from which the geophysical products needed by Earth science models can derive. Then, the service discovers relevant data sources which meet these requirements, schedules data acquisition by ordering and retrieving data from these sources, and processes and returns the customized data which will be used in the Earth science models. In this general framework, Sensor Web Enablement specifications of Open Geospatial Consortium are implemented and integrated with other open Web geospatial data services seamlessly to access real or virtual Earth observation sensors. Additionally, an Ajax-enabled browser client is built to facilitate the standard sensor Web operations, user account management along with data acquisition. The prototype system is demonstrated to retrieve near real-time satellite observation data from Comprehensive Large Array-data Stewardship System through standard Web services, and also is applied in the bird migration model which uses climate data, vegetation data, as well as other remotely sensed and in-situ observation data to determine the bird migration routes and identify the possible resting areas.