The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

AbstractThis paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.

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A 1DVAR-Based Snowfall Rate Algorithm for Passive Microwave Radiometers

Advances in Global Change Research - Satellite Precipitation Measurement ◽

10.1007/978-3-030-24568-9_17 ◽

2020 ◽

pp. 297-313

Author(s):

Huan Meng ◽

Cezar Kongoli ◽

Ralph R. Ferraro

Keyword(s):

Passive Microwave ◽

Microwave Radiometers

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Radiative properties of deformed hydrometeors for commonly used passive microwave frequencies

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.7689 ◽

1988 ◽

Vol 26 (5) ◽

pp. 629-638 ◽

Cited By ~ 11

Author(s):

C.D. Kummerow ◽

J.A. Weinman

Keyword(s):

Passive Microwave ◽

Radiative Properties ◽

Microwave Frequencies

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Combined Conduction and Radiation Heat Transfer in Porous Materials Heated by Intense Solar Radiation

Journal of Solar Energy Engineering ◽

10.1115/1.3267649 ◽

1985 ◽

Vol 107 (1) ◽

pp. 29-34 ◽

Cited By ~ 42

Author(s):

L. K. Matthews ◽

R. Viskanta ◽

F. P. Incropera

Keyword(s):

Heat Transfer ◽

Radiation Heat Transfer ◽

Plane Layer ◽

Single Scattering ◽

Dominant Mode ◽

Radiative Properties ◽

Maximum Temperature ◽

Transfer Model ◽

Radiation Heat ◽

Flux Approximation

An analysis is presented to predict the heat transfer characteristics of a plane layer of a semitransparent, high-temperature, porous material which is irradiated by an intense solar flux. A transient, combined conduction and radiation heat transfer model, which is based on a two-flux approximation for the radiation, is used to predict the temperature distribution and heat transfer in the material. Numerical results have been obtained using thermophysical and radiative properties of zirconia as a typical material. The results show that radiation is an important mode of heat transfer, even when the opacity of the material is large (τL > 100). Radiation is the dominant mode of heat transfer in the front third of the material and comparable to conduction toward the back. The semitransparency and high single scattering albedo of the zirconia combine to produce a maximum temperature in the interior of the material.

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Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset

10.20944/preprints201811.0217.v1 ◽

2018 ◽

Author(s):

Alan K Betts ◽

Raymond L Desjardins

Keyword(s):

Snow Cover ◽

Diurnal Cycle ◽

Radiative Forcing ◽

Potential Temperature ◽

Warm Season ◽

Growing Season ◽

Cold Season ◽

Maximum Temperature ◽

Canadian Prairie ◽

Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land-atmosphere-cloud coupling. The key reason is that trained observers made hourly estimates of opaque cloud fraction that obscures the sun, moon or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality that they can be calibrated against Baseline Surface Radiation Network data to give the climatology of the daily short-wave, longwave and total cloud forcing (SWCF, LWCF and CF). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing reverses sign from negative in the warm season to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10°C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature and the pressure height of the lifting condensation level are all tightly coupled to opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between cloud forcing and the warm season imbalance of the diurnal cycle; which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forci, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of mixing ratio which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season and its dependence on reflective cloud.

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Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

10.5194/tc-2016-126 ◽

2016 ◽

Author(s):

Libo Wang ◽

Peter Toose ◽

Ross Brown ◽

Chris Derksen

Keyword(s):

Snow Cover ◽

Microwave Radiometer ◽

Surface Air Temperature ◽

Arctic Region ◽

Temporal Variations ◽

Passive Microwave ◽

The Arctic ◽

Winter Period ◽

Strongly Correlated ◽

Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

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Development of a precipitation-retrieval scheme for cross-track passive microwave sounding instruments

10.1016/b978-0-12-822973-6.00009-3 ◽

2022 ◽

pp. 287-304

Author(s):

Chris Kidd ◽

Toshihisa Matsui ◽

Sarah Ringerud

Keyword(s):

Passive Microwave ◽

Retrieval Scheme ◽

Microwave Sounding ◽

Cross Track

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Spectral linear mixing model applied to data from passive microwave radiometers for sea ice mapping in the Antarctic Peninsula

Geocarto International ◽

10.1080/10106049.2020.1856194 ◽

2021 ◽

pp. 1-27

Author(s):

Fernando Luis Hillebrand ◽

Ulisses Franz Bremer ◽

Marcos Wellausen Dias de Freitas ◽

Juliana Costi ◽

Cláudio Wilson Mendes Júnior ◽

...

Keyword(s):

Sea Ice ◽

Antarctic Peninsula ◽

Mixing Model ◽

Passive Microwave ◽

Linear Mixing Model ◽

Microwave Radiometers ◽

The Antarctic

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Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Environments ◽

10.3390/environments5120129 ◽

2018 ◽

Vol 5 (12) ◽

pp. 129 ◽

Cited By ~ 1

Author(s):

Alan Betts ◽

Raymond Desjardins

Keyword(s):

Snow Cover ◽

Diurnal Cycle ◽

Radiative Forcing ◽

Potential Temperature ◽

Warm Season ◽

Growing Season ◽

Cold Season ◽

Maximum Temperature ◽

Canadian Prairie ◽

Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

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Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0051.1 ◽

2015 ◽

Vol 17 (1) ◽

pp. 383-400 ◽

Cited By ~ 44

Author(s):

Chris Kidd ◽

Toshihisa Matsui ◽

Jiundar Chern ◽

Karen Mohr ◽

Chris Kummerow ◽

...

Keyword(s):

Western Europe ◽

The United States ◽

Passive Microwave ◽

The Arctic ◽

Temporal Sampling ◽

Physically Based ◽

Retrieval Scheme ◽

Cross Track ◽

Global Precipitation ◽

Mean Square Errors

Abstract The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

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