Information content on hydrometeors from millimeter and sub-millimeter wavelengths

ABSTRACT This study examines the information content on hydrometeors that could be provided by a future HYperspectral Microwave Sensor (HYMS) with frequencies ranging from 6.9 to 874 GHz (millimeter and sub-millimeter regions). Through optimal estimation theory the information content is expressed quantitatively in terms of degrees of freedom for signal (DFS). For that purpose the Atmospheric Radiative Transfer Simulator (ARTS) and its Jacobians are used with a set of 25 cloudy and precipitating profiles and their associated errors from the European Centre for Medium-range Weather Forecasting (ECMWF) global numerical weather prediction model. In agreement with previous studies it is shown that frequencies between 10 and 40 GHz are the most informative ones for liquid and rain water contents. Similarly, the absorption band at 118 GHz contains significant information on liquid precipitation. A set of new window channels (15.37-, 40.25-, 101-GHz) could provide additional information on the liquid phase. The most informative channels on cloud ice water are the window channels at 664 and 874 GHz and the water vapour absorption bands at 325 and 448 GHz. Regarding snow water contents, the channels having the largest DFS values are located in window regions (150-, 251-, 157-, 101-GHz). However it is necessary to consider 90 channels in order to represent 90% of the DFS. The added value of HYMS has been assessed against current Special Sensor Microwave Imager/Sounder (SSMI/S) onboard the Defense Meteorological Satellite Program (DMSP) and future (Microwave Imager/Ice Cloud Imager (MWI/ICI) onboard European Polar orbiting Satellite – Second Generation (EPS-SG)) microwave sensors. It appears that with a set of 276 channels the information content on hydrometeors would be significantly enhanced: the DFS increases by 1.7 against MWI/ICI and by 3 against SSMI/S. A number of tests have been performed to examine the robustness of the above results. The most informative channels on solid hydrometeors remain the same over land and over ocean surfaces. On the other hand, the database is not large enough to produce robust results over land surfaces for liquid hydrometeors. The sensitivity of the results to the microphysical properties of frozen hydrometeors has been investigated. It appears that a change in size distribution and scattering properties can move the large information content of the channels at 664 and 874 GHz from cloud ice to solid precipitation.

[1]  Filipe Aires,et al.  A Tool to Estimate Land‐Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction , 2011 .

[2]  Alan J. Geer,et al.  Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies , 2014 .

[3]  V. O. John,et al.  The impact of ozone lines on AMSU‐B radiances , 2004 .

[4]  S. Fox,et al.  ISMAR: A new Submillimeter Airborne Radiometer , 2014, 2014 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad).

[5]  P. Bauer,et al.  Passive microwave radiometer channel selection basedoncloudandprecipitation information content , 2006 .

[6]  T. R. Sreerekha,et al.  A concept for a satellite mission to measure cloud ice water path, ice particle size, and cloud altitude , 2007 .

[7]  F. Rabier,et al.  Microwave land emissivity and skin temperature for AMSU‐A and ‐B assimilation over land , 2006 .

[8]  Stefan Buehler,et al.  Atmospheric Chemistry and Physics 3-D polarised simulations of space-borne passive mm / sub-mm midlatitude cirrus observations : a case study , 2007 .

[9]  Florence Rabier,et al.  Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas , 2004 .

[10]  S. A. Buehlera,et al.  ARTS , the atmospheric radiative transfer simulator , 2004 .

[11]  Philippe Lopez,et al.  A convection scheme for data assimilation: Description and initial tests , 2005 .

[12]  C. Emde,et al.  ARTS, the atmospheric radiative transfer simulator, version 2 , 2011 .

[13]  N. Bormann,et al.  All-sky assimilation of microwave humidity sounders , 2016 .

[14]  P. Bauer,et al.  Hydrometeor Retrieval Accuracy Using Microwave Window and Sounding Channel Observations , 2005 .

[15]  Catherine Prigent,et al.  Comparisons of the Millimeter and Submillimeter Bands for Atmospheric Temperature and Water Vapor Soundings for Clear and Cloudy Skies , 2006 .

[16]  Susanne Crewell,et al.  Observing ice clouds in the submillimeter spectral range: the CloudIce mission proposal for ESA's Earth Explorer 8 , 2012 .

[17]  Antonia Gambacorta,et al.  Evaluation of a revised IASI channel selection for cloudy retrievals with a focus on the Mediterranean basin , 2014 .

[18]  Oliver Lemke,et al.  Absorption lookup tables in the radiative transfer model ARTS , 2011 .

[19]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[20]  Jana Mendrok,et al.  On the microwave optical properties of randomly oriented ice hydrometeors , 2014 .

[21]  Tim J. Hewison,et al.  Radiometric characterization of AMSU-B , 1995 .

[22]  Yann Michel,et al.  Heterogeneous Convective-Scale Background Error Covariances with the Inclusion of Hydrometeor Variables , 2011 .

[23]  Sid-Ahmed Boukabara,et al.  Benefits of a hyperspectral microwave sensor , 2011, 2011 IEEE SENSORS Proceedings.

[24]  Richard T. Marriott,et al.  Forecast sensitivity to observations in the Met Office Global numerical weather prediction system , 2014 .

[25]  Peter Bauer,et al.  Multiple‐scattering microwave radiative transfer for data assimilation applications , 2006 .

[26]  Loïk Berre,et al.  Diagnosis and formulation of heterogeneous background‐error covariances at the mesoscale , 2010 .

[27]  William J. Blackwell,et al.  Hyperspectral Microwave Atmospheric Sounding , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Andrew Collard,et al.  Selection of IASI channels for use in numerical weather prediction , 2007 .

[29]  Patrick Eriksson,et al.  Efficient forward modelling by matrix representation of sensor responses , 2006 .

[30]  Claudia Emde,et al.  A polarized discrete ordinate scattering model for simulations of limb and nadir long‐wave measurements in 1‐D/3‐D spherical atmospheres , 2004 .

[31]  Thibaut Montmerle,et al.  Heterogeneous background‐error covariances for the analysis and forecast of fog events , 2011 .

[32]  P. Bauer,et al.  A Revised Cloud Overlap Scheme for Fast Microwave Radiative Transfer in Rain and Cloud , 2009 .

[33]  J. Derber,et al.  A reformulation of the background error covariance in the ECMWF global data assimilation system , 1999 .

[34]  D. L. Huber,et al.  Absorption, emission, and linebreadths: A semihistorical perspective , 1977 .

[35]  Martin Kuntz,et al.  Efficient line-by-line calculation of absorption coefficients , 1999 .

[36]  Andrew J. Heymsfield,et al.  Parameterization of Tropical Cirrus Ice Crystal Size Distributions and Implications for Radiative Transfer: Results from CEPEX , 1997 .

[37]  Zsófia Osváth,et al.  DOI: 10 , 2011 .

[38]  Jean-Luc Moncet,et al.  Development and recent evaluation of the MT_CKD model of continuum absorption , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  Catherine Prigent,et al.  The Assimilation of Observations from the Advanced Microwave Sounding Unit over Sea Ice in the French Global Numerical Weather Prediction System , 2014 .

[40]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[41]  Clive D. Rodgers.  Information content and optimization of high-spectral-resolution measurements , 1996, Optics + Photonics.

[42]  Adrian M. Tompkins,et al.  A cloud scheme for data assimilation: Description and initial tests , 2004 .

[43]  P. Bauer,et al.  Enhanced use of all-sky microwave observations sensitive to water vapour , cloud and precipitation , 2010 .

[44]  P. Field,et al.  Snow Size Distribution Parameterization for Midlatitude and Tropical Ice Clouds , 2007 .

[45]  Peter Bauer,et al.  Implementation of 1D+4D‐Var assimilation of precipitation‐affected microwave radiances at ECMWF. II: 4D‐Var , 2006 .

[46]  Filipe Aires,et al.  Information content on temperature and water vapour from a hyper‐spectral microwave sensor , 2015 .

[47]  William S. Olson,et al.  A physical model to estimate snowfall over land using AMSU‐B observations , 2008 .

[48]  T. R. Sreerekhaa,et al.  A strong ice cloud event as seen by a microwave satellite sensor : Simulations and observations , 2008 .

[49]  Iasonas F. Triantis,et al.  A novel front-end for impedance spectroscopy , 2011, 2011 IEEE SENSORS Proceedings.

[50]  Alan E. Lipton,et al.  Satellite sounding channel optimization in the microwave spectrum , 2003, IEEE Trans. Geosci. Remote. Sens..

[51]  J. M. Sabater,et al.  Impact of satellite data , 2013 .

[52]  Gang Li,et al.  The HITRAN 2008 molecular spectroscopic database , 2005 .

[53]  Florence Rabier,et al.  Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances , 2002 .

[54]  Jean-Noël Thépaut,et al.  Evaluation of the AIRS near‐real‐time channel selection for application to numerical weather prediction , 2003 .

[55]  E. R. Polovtseva,et al.  The HITRAN2012 molecular spectroscopic database , 2013 .

[56]  S. A. Buehlera,et al.  Recent developments in the line-by-line modeling of outgoing longwave radiation , 2005 .

[57]  C. Cardinali.  Monitoring the observation impact on the short‐range forecast , 2009 .

[58]  Guosheng Liu,et al.  A database of microwave single-scattering properties for nonspherical ice particles , 2008 .

[59]  P. Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part II: Assessment , 2010 .

[60]  Philippe Lopez,et al.  Linearized Physics for Data Assimilation at ECMWF , 2013 .

[61]  Florence Rabier,et al.  Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part II: Impacts of Assimilating Surface-Sensitive Channels on the African Monsoon during AMMA , 2010 .

[62]  Quanhua Liu,et al.  Scattering database in the millimeter and submillimeter wave range of 100–1000 GHz for nonspherical ice particles , 2009 .

[63]  Philippe Lopez,et al.  Direct 4D-Var Assimilation of NCEP Stage IV Radar and Gauge Precipitation Data at ECMWF , 2011 .

[64]  J. Miao,et al.  DEVELOPMENT AND CALIBRATION OF A K-BAND GROUND-BASED HYPERSPECTRAL MICROWAVE RADIOMETER FOR WATER VAPOR MEASUREMENTS , 2013 .

[65]  Peter Bauer,et al.  488 Implementation of 1 D + 4 D-Var Assimilation of Precipitation Affected Microwave Radiances at ECMWF , Part II : 4 D-Var , 2006 .