Introduction to Suomi national polar‐orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications

Weng, Fuzhong; Zou, Xiaolei; Wang, X.; Yang, Shengpeng; Goldberg, Mitch

doi:10.1029/2012jd018144

Cited by 109 publications

(73 citation statements)

References 29 publications

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“…VIIRS is one of the five instruments (others are the Advanced Technology Microwave Sounder [32], the Cross-Track Infrared Sounder [33], the Ozone Mapping Profiler Suite [34,35], and the Clouds and the Earth's Radiant Energy System [36,37]) on the satellite, and is configured to collect visible and infrared imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans [38][39][40].The first global NPP-VIIRS nighttime light data were generated by the Earth Observation Group, NOAA National Geophysical Data Center. VIIRS day/night band data collected on nights with zero moonlight during 18-26 April 2012 and 11-23 October 2012 was composited into a single dataset [31,[41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

et al. 2014

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Abstract:The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the OPEN ACCESSRemote Sens. 2014, 6 1706 GDP and EPC (which is from the country's statistical data) at provincial-and prefectural-level divisions of mainland China. The result of the linear regression shows that R 2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

et al. 2014

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“…Special functions of TBs already proposed for rainfall retrieval (Kidd, 1998;Ferraro and Marks, 1995;Grody, 1991), such as the polarization corrected temperature (PCT 85 ) and the scattering index, have also been considered as the NN inputs (Sarma et al, 2008;Mahesh et al, 2011). Some geographical and meteorological parameters (e.g., surface type, surface height, season, latitude) are often considered as auxiliary input data in order to reduce the ambiguity intrinsic to the PMW precipitation retrievals based only on observed TBs (e.g., Panegrossi et al, 1998;Kummerow et al, 2011;You and Liou, 2012;You et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…2 The ATMS radiometer ATMS is a total power cross-track scanning microwave radiometer on board the NPP satellite (and JPSS satellites scheduled for early 2017), with a swath of 2600 km, angular span of ±52.77 • relative to nadir (Boukabara et al, , 2013Weng et al, 2012;Goldberg et al, 2013;Zou et al 2013). During each scan the Earth is viewed at 96 different angles, sampled every 1.11 • .…”

Section: Introductionmentioning

confidence: 99%

“…There are slight differences in the frequencies of ATMS channels 88.2, 165.5, and 183.31 ± 7.0 GHz with respect to the corresponding MHS channels (89.0,157.0,and 190.31 GHz). Three new channels are added compared to AMSU/MHS: channel 4 (51.76 GHz) for lower tropospheric temperature sounding and the two channels 19 and 21 (183.31 ± 4.5 and 183.31 ± 1.8 GHz) to enhance the moisture profiling performance, improving the vertical resolution, and potentially very useful also for precipitation (Surussavadee et al, 2012;Weng et al, 2012;Zou et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

et al. 2016

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Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitationsensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas.The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013-2014) dataset of coincident observations over a regular grid at 0.5 • × 0.5 • resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h −1 over ocean and 1.11 mm h −1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h −1 over vegetated land and 0.71 mm h −1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

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“…The swath width is 2600 km. ATMS combines all the channels of the preceding AMSU-A1, AMSU-A2, and AMSU-B sensors into a single package with considerable savings in mass, power, and volume [Weng et al, 2012[Weng et al, , 2013. Table 1 shows the ATMS channel characteristics.…”

Section: Atms and Crismentioning

confidence: 99%

Joint Polar Satellite System: The United States next generation civilian polar‐orbiting environmental satellite system

Goldberg¹,

Kilcoyne²,

Cikanek³

et al. 2013

JGR Atmospheres

193

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NOAA's next generation polar‐orbiting environmental satellite system, designated as the Joint Polar Satellite System (JPSS), was proposed in February 2010, as part of the President's Fiscal Year 2011 budget request, to be the Civilian successor to the restructured National Polar‐Orbiting Operational Environmental Satellite System (NPOESS). Beginning 1 October 2013, the JPSS baseline consists of a suite of five instruments: advanced microwave and infrared sounders critical for short‐ and medium‐range weather forecasting; an advanced visible and infrared imager needed for environmental assessments such as snow/ice cover, droughts, volcanic ash, forest fires and surface temperature; ozone sensor primarily used for global monitoring of ozone and input to weather and climate models; and an Earth radiation budget sensor for monitoring the Earth's energy budget. NASA will fund the Earth radiation budget sensor and the ozone limb sensor for the second JPSS operational satellite—JPSS‐2. JPSS is implemented through a partnership between NOAA and the U.S. National Aeronautics and Space Administration (NASA). NOAA is responsible for overall funding; maintaining the high‐level requirements; establishing international and interagency partnerships; developing the science and algorithms, and user engagement; NOAA also provides product data distribution and archiving of JPSS data. NASA's role is to serve as acquisition Center of Excellence, providing acquisition of instruments, spacecraft and the multimission ground system, and early mission implementation through turnover to NOAA for operations.

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Introduction to Suomi national polar‐orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications

Cited by 109 publications

References 29 publications

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

Joint Polar Satellite System: The United States next generation civilian polar‐orbiting environmental satellite system

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