@article{ROSSOW2024100004,

title = {Evolution of the concept of cloud-climate feedbacks},

author = {William B. Rossow},

url = {https://www.williambrossow.com/wp-content/uploads/2024/11/Evolution-of-the-concept-of-cloud-cli_2024_Journal-of-the-European-Meteorolo.pdf},

doi = {https://doi.org/10.1016/j.jemets.2024.100004},

issn = {2950-6301},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of the European Meteorological Society},

volume = {1},

pages = {100004},

abstract = {The early concept of cloud-climate feedback was formulated as involving solely cloud cover effects on planetary radiation, separate from precipitation, and consistent with simple climate models. However, more than 50 years later, this concept continues to dominate analyses, especially of climate model performance, even though multiple global data products now exist that quantify weather-to-decadal scale joint variations of cloud properties, radiative fluxes, precipitation, surface energy and water fluxes, atmospheric and surface properties, and the circulations of the atmosphere and ocean. A more complete, observation-based analysis of cloud feedbacks on weather, seasonal and interannual scales is now possible. Results to date indicate that the cloud-radiative feedback amplifies the positive cloud-precipitation feedback on the atmospheric circulation from weather-to-annual time scales. Further analysis extensions are suggested.},

keywords = {PRECIPITATION, Radiation, Weather},

pubstate = {published},

tppubtype = {article}

}

@article{Schroder2018-ya,

title = {The GEWEX Water Vapor Assessment archive of water vapour  products from satellite observations and reanalyses},

author = {Marc Schr\"{o}der and Maarit Lockhoff and Frank Fell and John Forsythe and Tim Trent and Ralf Bennartz and Eva Borbas and Michael G Bosilovich and Elisa Castelli and Hans Hersbach and Misako Kachi and Shinya Kobayashi and E Robert Kursinski and Diego Loyola and Carl Mears and Rene Preusker and William B Rossow and Suranjana Saha},

url = {https://www.williambrossow.com/wp-content/uploads/2022/03/essd-10-1093-2018.pdf, Download file},

doi = {10.5194/essd-10-1093-2018},

year  = {2018},

date = {2018-06-01},

urldate = {2018-06-01},

journal = {Earth Syst. Sci. Data},

volume = {10},

number = {2},

pages = {1093--1117},

publisher = {Copernicus GmbH},

abstract = {The Global Energy and Water cycle Exchanges (GEWEX) Data and 

 Assessments Panel (GDAP) initiated the GEWEX Water Vapor 

 Assessment (G-VAP), which has the main objectives to quantify 

 the current state of art in water vapour products being 

 constructed for climate applications and to support the 

 selection process of suitable water vapour products by GDAP for 

 its production of globally consistent water and energy cycle 

 products. During the construction of the G-VAP data archive, 

 freely available and mature satellite and reanalysis data 

 records with a minimum temporal coverage of 10 years were 

 considered. The archive contains total column water vapour 

 (TCWV) as well as specific humidity and temperature at four 

 pressure levels (1000, 700, 500, 300 hPa) from 22 different data 

 records. All data records were remapped to a regular 

 longitude/latitude grid of 2°x2°. The archive consists of four 

 different folders: 22 TCWV data records covering the period 

 2003-2008, 11 TCWV data records covering the period 1988-2008, 

 as well as seven specific humidity and seven temperature data 

 records covering the period 1988-2009. The G-VAP data archive is 

 referenced under the following digital object identifier (doi): 

 http://dx.doi.org/10.5676/EUM SAF CM/GVAP/V001. Within G-VAP, 

 the characterisation of water vapour products is, among other 

 ways, achieved through intercomparisons of the considered data 

 records, as a whole and grouped into three classes of 

 predominant retrieval condition: clear-sky, cloudy-sky and 

 all-sky. Associated results are shown using the 22 TCWV data 

 records. The standard deviations among the 22 TCWV data records 

 have been analysed and exhibit distinct maxima over central 

 Africa and the tropical warm pool (in absolute terms) as well as 

 over the poles and mountain regions (in relative terms). The 

 variability in TCWV within each class can be large and prohibits 

 conclusions on systematic differences in TCWV between the 

 classes.},

keywords = {PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{Luo2017-bx,

title = {Tropical cloud and precipitation regimes as seen from  near‐simultaneous TRMM, CloudSat, and CALIPSO observations  and comparison with ISCCP},

author = {Zhengzhao Johnny Luo and Ricardo C Anderson and William B Rossow and Hanii Takahashi},

url = {https://www.williambrossow.com/wp-content/uploads/2022/03/JGR-Atmospheres-2017-Luo-Tropical-cloud-and-precipitation-regimes-as-seen-from-near‐simultaneous-TRMM-CloudSat-and.pdf, Download file},

doi = {10.1002/2017jd026569},

year  = {2017},

date = {2017-06-01},

urldate = {2017-06-01},

journal = {J. Geophys. Res.},

volume = {122},

number = {11},

pages = {5988--6003},

publisher = {\"{A}merican Geophysical Union (AGU)},

abstract = {\"{A}lthough Tropical Rainfall Measuring Mission (TRMM) and 

 CloudSat/CALIPSO fly in different orbits, they frequently cross 

 each other so that for the period between 2006 and 2010, a total 

 of 15,986 intersect lines occurred within 20 min of each other 

 from 30°S to 30°N, providing a rare opportunity to study 

 tropical cloud and precipitation regimes and their internal 

 vertical structure from near‐simultaneous measurements by these 

 active sensors. A k‐means cluster analysis of TRMM and CloudSat 

 matchups identifies three tropical cloud and precipitation 

 regimes: the first two regimes correspond to, respectively, 

 organized deep convection with heavy rain and cirrus anvils with 

 moderate rain; the third regime is a convectively suppressed 

 regime that can be further divided into three subregimes, which 

 correspond to, respectively, stratocumulus clouds with drizzle, 

 cirrus overlying low clouds, and nonprecipitating cumulus. 

 Inclusion of CALIPSO data adds to the dynamic range of cloud 

 properties and identifies one more cluster; subcluster analysis 

 further identifies a thin, midlevel cloud regime associated with 

 tropical mountain ranges. The radar‐lidar cloud regimes are 

 compared with the International Satellite Cloud Climatology 

 Project (ISCCP) weather states (WSs) for the extended tropics. 

 Focus is placed on the four convectively active WSs, namely, 

 WS1--WS4. ISCCP WS1 and WS2 are found to be counterparts of 

 Regime 1 and Regime 2 in radar‐lidar observations, respectively. 

 ISCCP WS3 and WS4, which are mainly isolated convection and 

 broken, detached cirrus, do not have a strong association with 

 any individual radar and lidar regimes, a likely effect of the 

 different sampling strategies between ISCCP and active sensors 

 and patchy cloudiness of these WSs."},

keywords = {PRECIPITATION, WATER VAPOR, WEATHER STATE},

pubstate = {published},

tppubtype = {article}

}

@article{ta02100m,

title = {Increases in tropical rainfall driven by changes in frequency of organized deep convection},

author = {J. Tan and C. Jakob and W. B. Rossow and G. Tselioudis},

url = {https://www.williambrossow.com/wp-content/uploads/2022/09/2015_tanetal.nature14339.pdf, Download file},

doi = {10.1038/nature14339},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Nature},

volume = {519},

pages = {451--454},

keywords = {PRECIPITATION, WATER VAPOR, WEATHER STATE},

pubstate = {published},

tppubtype = {article}

}

@article{Shahroudi2014-on,

title = {Using land surface microwave emissivities to isolate the  signature of snow on different surface types},

author = {Narges Shahroudi and William Rossow},

url = {https://www.williambrossow.com/wp-content/uploads/2022/09/2014_Shahroudi_Rossow.1-s2.0-S0034425714002533-main.pdf, Download file},

doi = {10.1016/j.rse.2014.07.008},

year  = {2014},

date = {2014-09-01},

urldate = {2014-09-01},

journal = {Remote Sens. Environ.},

volume = {152},

pages = {638--653},

publisher = {Elsevier BV},

abstract = {The objective of this paper is to better isolate the snow 

 signature in microwave signals to be able to explore the ability 

 of satellite microwave measurements to determine snowpack 

 properties. The surface microwave effective emissivities used in 

 this study are derived from SSM/I passive microwave observations 

 by removing the contributions of the cloud and atmosphere and 

 then separating out the surface temperature variations using 

 ancillary atmospheric, cloud and surface data. The sensitivity 

 of the effective emissivity to the presence/absence of snow is 

 evaluated for the Northern Hemisphere. The effect of the 

 presence of snow, the variation of land types, and temperature 

 on the emissivities have been examined by observing the temporal 

 and spatial variability of these measurements between 19 and 85 

 GHz over the Northern Hemisphere. The time-anomaly of 

 differences between effective emissivity at 19 V and 85 V 

 enabled the constant effects of land surface vegetation 

 properties to be removed to isolate the snow signature. The 

 resulting 12-year snow signal combined with skin temperature 

 data can detect the existence of snow cover over the Northern 

 Hemisphere on daily basis. The results of this method compared 

 with the operational NOAA weekly snow cover maps agree at 90% 

 of locations and times. Most of the disagreements could be 

 explained by rapid evolution of snow emissivities associated 

 with freeze--melt--refreeze cycles and precipitation (snowfall), 

 and some of them by the space--time resolution differences of 

 the microwave and operational snow cover determinations. These 

 results compared with the NISE, NOAA IMS, CMC, and MODIS, and 

 snow products agree within 78% to 92%.},

keywords = {LAND HYDROLOGY, PRECIPITATION, SURFACE PROPERTIES, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{Lakhankar2013-zx,

title = {CREST-Snow Field Experiment: analysis of snowpack properties  using multi-frequency microwave remote sensing data},

author = {T Y Lakhankar and J Mu noz and P Romanov and A M Powell and N Y Krakauer and W B Rossow and R M Khanbilvardi},

url = {https://www.williambrossow.com/wp-content/uploads/2022/03/hess-17-783-2013.pdf, Download file},

doi = {10.5194/hess-17-783-2013},

year  = {2013},

date = {2013-02-01},

urldate = {2013-02-01},

journal = {Hydrol. Earth Syst. Sci.},

volume = {17},

number = {2},

pages = {783--793},

publisher = {Copernicus GmbH},

abstract = {\"{A}bstract. The CREST-Snow Analysis and Field Experiment 

 (CREST-SAFE) was carried out during January--March 2011 at the 

 research site of the National Weather Service office, Caribou, 

 ME, USA. In this experiment dual-polarized microwave (37 and 89 

 GHz) observations were accompanied by detailed synchronous 

 observations of meteorology and snowpack physical properties. 

 The objective of this long-term field experiment was to improve 

 understanding of the effect of changing snow characteristics 

 (grain size, density, temperature) under various meteorological 

 conditions on the microwave emission of snow and hence to 

 improve retrievals of snow cover properties from satellite 

 observations. In this paper we present an overview of the field 

 experiment and comparative preliminary analysis of the 

 continuous microwave and snowpack observations and simulations. 

 The observations revealed a large difference between the 

 brightness temperature of fresh and aged snowpack even when the 

 snow depth was the same. This is indicative of a substantial 

 impact of evolution of snowpack properties such as snow grain 

 size, density and wetness on microwave observations. In the 

 early spring we frequently observed a large diurnal variation in 

 the 37 and 89 GHz brightness temperature with small 

 depolarization corresponding to daytime snowmelt and nighttime 

 refreeze events. SNTHERM (SNow THERmal Model) and the HUT 

 (Helsinki University of Technology) snow emission model were 

 used to simulate snowpack properties and microwave brightness 

 temperatures, respectively. Simulated snow depth and snowpack 

 temperature using SNTHERM were compared to in situ observations. 

 Similarly, simulated microwave brightness temperatures using the 

 HUT model were compared with the observed brightness 

 temperatures under different snow conditions to identify 

 different states of the snowpack that developed during the 

 winter season."},

keywords = {LAND HYDROLOGY, PRECIPITATION, SURFACE PROPERTIES, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2013-mb,

title = {The precipitation characteristics of ISCCP tropical weather  states},

author = {Dongmin Lee and Lazaros Oreopoulos and George J Huffman and William B Rossow and In-Sik Kang},

url = {https://www.williambrossow.com/wp-content/uploads/2022/03/15200442-Journal-of-Climate-The-Precipitation-Characteristics-of-ISCCP-Tropical-Weather-States.pdf, Download file},

doi = {10.1175/jcli-d-11-00718.1},

year  = {2013},

date = {2013-02-01},

urldate = {2013-02-01},

journal = {J. Clim.},

volume = {26},

number = {3},

pages = {772--788},

publisher = {\"{A}merican Meteorological Society},

abstract = {\"{A}bstract The authors examine the daytime precipitation 

 characteristics of the International Satellite Cloud Climatology 

 Project (ISCCP) weather states in the extended tropics 

 (35°S--35°N) for a 10-yr period. The main precipitation dataset 

 used is the Tropical Rainfall Measuring Mission (TRMM) 

 Multisatellite Precipitation Analysis operational product 3B42 

 dataset, but Global Precipitation Climatology Project daily data 

 are also used for comparison. It is found that the most 

 convectively active ISCCP weather state (WS1), despite an 

 occurrence frequency below 10%, is the most dominant state with 

 regard to surface precipitation, producing both the largest mean 

 precipitation rates when present and the largest percent 

 contribution to the total precipitation of the tropics; yet, 

 even this weather state appears to not precipitate about half 

 the time, although this may be to some extent an artifact of 

 detection and spatiotemporal matching limitations of the 

 precipitation dataset. WS1 exhibits a modest annual cycle of the 

 domain-average precipitation rate, but notable seasonal shifts 

 in its geographic distribution. The precipitation rates of the 

 other weather states appear to be stronger when occurring before 

 or after WS1. The precipitation rates of the various weather 

 states are different between ocean and land, with WS1 producing 

 higher daytime rates on average over ocean than land, likely 

 because of the larger size and more persistent nature of oceanic 

 WS1s. The results of this study, in addition to advancing the 

 understanding of tropical hydrology, can serve as higher-order 

 diagnostics for evaluating the realism of tropical precipitation 

 distributions in large-scale models."},

keywords = {PRECIPITATION, WATER VAPOR, WEATHER STATE},

pubstate = {published},

tppubtype = {article}

}

@article{pr02400t,

title = {Comparisons of the millimeter and submillimeter bands for atmospheric temperature and water vapor soundings for clear and cloudy skies},

author = {C. Prigent and J. R. Pardo and W. B. Rossow},

url = {https://www.williambrossow.com/wp-content/uploads/2022/05/2006_Prigent_pr02400t.pdf, Download file},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {J. Appl. Meteorol. Climatol.},

volume = {45},

pages = {1622--1633},

keywords = {PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{ai07000g,

title = {Remote sensing from the infrared atmospheric sounding interferometer instrument. 2. Simultaneous retrieval of temperature, water vapor and ozone atmospheric profiles},

author = {F. Aires and W. B. Rossow and N. Scott and A. Chedin},

url = {https://www.williambrossow.com/wp-content/uploads/2022/06/2002_Aires_ai07000g.pdf, Download file},

doi = {10.1029/2001JD001591},

year  = {2002},

date = {2002-01-01},

urldate = {2002-01-01},

journal = {J. Geophys. Res.},

volume = {107},

number = {D22},

pages = {4620},

keywords = {ENERGETICS, FEEDBACKS, PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{es02000x,

title = {Comparison of upper tropospheric humidity retrievals from TOVS and Meteosat},

author = {C. Escoffier and J. J. Bates and A. Ch\'{e}9din and W. B. Rossow and J. Schmetz},

url = {https://www.williambrossow.com/wp-content/uploads/2022/06/Journal-of-Geophysical-Research-Atmospheres-2001-Escoffier-Comparison-of-upper-tropospheric-humidity-retrievals-from.pdf, Download file},

doi = {10.1029/2000JD900553},

year  = {2001},

date = {2001-01-01},

urldate = {2001-01-01},

journal = {J. Geophys. Res.},

volume = {106},

pages = {5227--5238},

keywords = {PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{ai04000r,

title = {A new neural network approach including first-guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature and emissivities over land from satellite microwave observations},

author = {F. Aires and C. Prigent and W. B. Rossow and M. Rothstein},

url = {https://www.williambrossow.com/wp-content/uploads/2022/06/2001_Aires_ai04000r.pdf, Download file},

doi = {10.1029/2001JD900085},

year  = {2001},

date = {2001-01-01},

urldate = {2001-01-01},

journal = {J. Geophys. Res.},

volume = {106},

pages = {14887--14907},

keywords = {ANALYSIS, ENERGETICS, FEEDBACKS, MODELS, PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}

@article{li06100c,

title = {Precipitation water path and rainfall rate estimates over oceans using special sensor microwave imager and International Satellite Cloud Climatology Project data},

author = {B. Lin and W. B. Rossow},

url = {https://www.williambrossow.com/wp-content/uploads/2022/08/1997_Lin_li06100c.pdf, Download file},

doi = {10.1029/96JD03987},

year  = {1997},

date = {1997-01-01},

urldate = {1997-01-01},

journal = {J. Geophys. Res.},

volume = {102},

pages = {9359--9374},

keywords = {PRECIPITATION, WATER VAPOR},

pubstate = {published},

tppubtype = {article}

}