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The air-sea flux of greenhouse gases [e.g., carbon dioxide (CO2)] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air-sea CO2 flux processing toolbox called the 'FluxEngine,' designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ, and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae Cloud allows users to easily exploit more than 8 TB of climate-quality Earth observation data for the derivation of gas fluxes. The resultant netCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, which verifies the air-sea CO2 flux calculations; demonstrates the use of the tools for studying global and shelf sea air-sea fluxes; and describes future developments. [ABSTRACT FROM AUTHOR]

This article presents the data collected and analyzed using the University of Iowa's X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA's Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol. [ABSTRACT FROM AUTHOR]

This study provides the first comprehensive quantitative detection efficiency (DE) estimation of the Met Office long-range very low-frequency (VLF) lightning location system, the Arrival Time Difference Network ATDnet, with a focus on cloud flash detection. ATDnet uses sensors in and around Europe to detect lightning in Europe, northern Africa, and northern parts of the Atlantic. In the present paper, ATDnet is validated against the Hydrological Cycle in the Mediterranean Experiment (HyMeX) Lightning Mapping Array (HyLMA) deployed in the south of France in autumn 2012 as a part of the HyMeX project Special Observation Period 1 (SOP1). Three storms (on 5, 11, and 25 September 2012) were selected for this study, as their activity was sufficient to provide good statistics but also low enough to avoid ambiguous situations due to temporally overlapping flashes. The overall ATDnet flash DE was approximately 89% for the 281 ground flashes [cloud to ground (CG)] and 24% for the 1324 cloud flashes [inter/intracloud (IC)] during the storms. It was also demonstrated that variations in cloud flash characteristics (e.g., flash vertical extent) have a significant impact on DE between and during the studied storms. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules-that is, Ku-only and Ka-only modules-and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB. [ABSTRACT FROM AUTHOR]

This paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures ( T b) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and T b statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the T b histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of T b biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed T b well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS-AVHRR observing record. [ABSTRACT FROM AUTHOR]

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land-ocean-coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions. The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land-ocean-coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region. [ABSTRACT FROM AUTHOR]

As atmospheric reflectance dominates top-of-the-atmosphere radiance over ocean, atmospheric correction is a critical component of ocean color retrievals. This paper explores the operational Sea-viewing Wide Field-of-view Sensor (SeaWiFS) algorithm atmospheric correction with ~13 000 coincident surface-based aerosol measurements. Aerosol optical depth at 440 nm (AOD440) is overestimated for AOD below ~0.1-0.15 and is increasingly underestimated at higher AOD; also, single-scattering albedo (SSA) appears overestimated when the actual value <~0.96. AOD440 and its spectral slope tend to be overestimated preferentially for coarse-mode particles. Sensitivity analysis shows that changes in these factors lead to systematic differences in derived ocean water-leaving reflectance ( Rrs) at 440 nm. The standard SeaWiFS algorithm compensates for AOD anomalies in the presence of nonabsorbing, medium-size-dominated aerosols. However, at low AOD and with absorbing aerosols, in situ observations and previous case studies demonstrate that retrieved Rrs is sensitive to spectral AOD and possibly also SSA anomalies. Stratifying the dataset by aerosol-type proxies shows the dependence of the AOD anomaly and resulting Rrs patterns on aerosol type, though the correlation with the SSA anomaly is too subtle to be quantified with these data. Retrieved chlorophyll- a concentrations (Chl) are affected in a complex way by Rrs differences, and these effects occur preferentially at high and low Chl values. Absorbing aerosol effects are likely to be most important over biologically productive waters near coasts and along major aerosol transport pathways. These results suggest that future ocean color spacecraft missions aiming to cover the range of naturally occurring and anthropogenic aerosols, especially at wavelengths shorter than 440 nm, will require better aerosol amount and type constraints. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

Clouds are both produced by and interact with the mesoscale and synoptic-scale structure of extratropical cyclones (ETCs) in ways that are still not well understood. Cloud-scale radiative and latent heating modifies the thermal environment, leading to a response in the dynamics that can in turn feed back on cloud distribution and microphysical properties. Key to the structure of ETCs is the warm conveyor belt (WCB); the poleward-ascending airstream that produces the bulk of the clouds and precipitation. This paper examines a long-lived WCB that persisted over the western North Atlantic Ocean in nearly the same location for several days. During this time, the storm was sampled multiple times by NASA's A-Train satellite constellation, and a clear transition from stratiform to convective clouds was observed. Examination of coincident temperature and water vapor data reveals destabilization of the thermodynamic profile after the cyclone reached maturity. CloudSat radar reflectivity from two sequential overpasses of the warm front depicts a change from stratiform to convective cloud structure, and high-frequency microwave data reveal an increase in the amount of ice hydrometeors. The presence of convection may serve to strengthen the warm frontal trough while slowing the movement of the primary low pressure center. The stratiform-convective transition cannot be detected from passive measurements of cloud-top pressure. The results demonstrate the effectiveness of multivariate satellite observations for examining the outcome of dynamic processes in ETCs, and highlight the need for more rapid temporal profiling in future remote sensing observing systems. [ABSTRACT FROM AUTHOR]

The Geostationary Operational Environmental Satellite-14 ( GOES-14) Imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of 2012, 2013, 2014, and 2015. This scan mode, known as the Super Rapid Scan Operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling that will be provided by the Advanced Baseline Imager on the next-generation GOES-R series. NOAA/National Weather Service/Storm Prediction Center (SPC) forecasters utilized the 1-min imagery extensively in operations when available over convectively active regions. They found it provided them with unique insight into relevant features and processes before, during, and after convective initiation. This paper introduces how the SRSOR datasets from GOES-14 were used by SPC forecasters and how these data are likely to be applied when available operationally from GOES-R. Several animations, included as supplemental material, showcase the rapid change of severe weather-related phenomena observed during the 2014 and 2015 SRSOR campaigns from the GOES-14 Imager. [ABSTRACT FROM AUTHOR]

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces-snow-covered lands, deserts, and coastal areas-are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges-Brahmaputra-Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year. [ABSTRACT FROM AUTHOR]

EUMETSAT has been deriving atmospheric motion vectors (AMV) operationally from the EUMETSAT Polar System satellite MetOp over polar regions since 2011. The launch of MetOp-B in 2012 permitted doubling the frequency of extracting AMVs using AVHRR imagery data of both MetOp-A and -B satellites. The tandem configuration with two satellites on the same orbital plane, but with a phase difference, provided an interesting opportunity to create global AMVs from the satellites with a significant overlap in imagery data. EUMETSAT has therefore developed a new global AVHRR winds product derived from a pair of MetOp-A and MetOp-B images that has been operational since January 2015. The temporal gap between the two images used for tracking clouds is about 50 min. The global coverage of this new wind product allows for a homogeneous retrieval of wind product over the whole globe, including the polar regions. This clearly helps fill the gaps between 55° and 70° latitude north and south, where only few wind observations are currently available for assimilation into numerical weather prediction models. The new global AVHRR wind product can be directly compared with AMVs derived from geostationary satellites. This paper describes the scientific concept of wind extraction using dual MetOp satellites. It highlights the performance of the new global AVHRR wind product by comparing with collocated AMVs extracted from the EUMETSAT geostationary satellites Meteosat-7 and Meteosat-10. [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Sounder (AIRS) level-1b radiances have been shown to be well calibrated (~0.3 K or higher) and have little secular drift (~4 mK yr−1) since operation started in September 2002. This paper investigates the linear trends of 10 years (2003-12) of AIRS global-mean radiances in the CO2 v2 band that are sensitive to emissions from the stratosphere (stratospheric channels). AIRS lower-stratospheric channels have a cooling trend of no more than 0.23 K decade−1 whereas the midstratospheric channels consistently show a statistically significant cooling trend as large as 0.58 K decade−1. The 95% confidence interval for the trend is ~±0.20 K decade−1. Two sets of synthetic AIRS radiances are computed using the principal component-based radiative transfer model (PCRTM), one based on a free-running GFDL Atmospheric Model, version 3 (AM3), over the same period and one based on ERA-Interim. The GFDL AM3 simulations overestimate the cooling trends in the mid- to upper-stratospheric channels but slightly underestimate them in the lower-stratospheric channels. The synthetic radiances based on ERA-Interim, however, have statistically significant positive trends at virtually all stratospheric channels. This confirms the challenge to the GCM modeling and reanalysis community to create a better simulation or assimilation of the stratospheric climate. It is shown that the linear trends in AIRS radiances can be reproduced to a large extent by the spectral radiative kernel technique and the trends from the AIRS L2 temperature retrievals and from the change of CO2. This suggests a closure between AIRS L1 radiances and L2 retrievals and the potential merit of AIRS data in studies of stratosphere changes. [ABSTRACT FROM AUTHOR]