All-sky radiancesimulationof Megha-TropiquesSAPHIR ...

J. Earth Syst. Sci. (2017) 126: 24 c© Indian Academy of SciencesDOI 10.1007/s12040-017-0805-3

All-sky radiance simulation of Megha-Tropiques SAPHIRmicrowave sensor using multiple scattering radiativetransfer model for data assimilation applications

A Madhulatha∗ , John P George and E N Rajagopal

National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences (MoES),A-50, Sector-62, Noida 201 309, India.

∗Corresponding author. e-mail: [email protected]

Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in dataassimilation system has a great potential in improving the accuracy of numerical model forecasts over theregions of high impact weather. By employing the multiple scattering radiative transfer model RTTOV-SCATT, all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channelmicrowave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry)sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance datain severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts areused as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and verticaldistribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiancesover cloudy regions during different stages of cyclone development. Simulated brightness temperatures ofsix SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is wellrepresented in all-sky computations. Improved correlation and reduced bias and root mean squareerror against SAPHIR observations are apparent. Probability distribution functions reveal that all-skysimulations are able to produce the cloud-affected lower brightness temperatures associated with cloudyregions. The density scatter plots infer that all-sky radiances are more consistent with observed radi-ances. Correlation between different types of hydrometeors and simulated brightness temperatures atrespective atmospheric levels highlights the significance of inclusion of scattering effects from differenthydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promis-ing and suggest that the inclusion of multiple scattering radiative transfer models into data assimilationsystem can simulate the cloud-affected microwave radiance data which provide detailed information onthree dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.

1. Introduction

Increase in quality and quantity of satellite dataplays a major role in improving the forecast per-formance of NWP (Numerical Weather Prediction)models (Rabier 2005). Microwave remote sensingof clouds has been recognised to be promising

when compared to visible and infrared regionsas microwaves can penetrate clouds and inter-act directly with hydrometeors. Passive microwavemeasurements from space-borne instruments offerthree-dimensional temperature and moisture infor-mation of atmosphere even in the presence ofclouds and precipitation and have great potential

Keywords. All-sky radiance simulation; Megha tropiques; microwave SAPHIR sensor; radiative transfer; data assimilation.

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in improving the global weather analyses andsubsequent model forecasts. Use of cloud clearsatellite radiances from infrared and microwavesounding data have already brought improvementsto moisture and temperature analyses (Eyre et al.1993; English et al. 2000).Assimilation of satellite radiances over cloudy

and precipitating regions is still a major challengein NWP. Cloud-affected radiances are not fullyexploited as it is difficult to separate the informa-tion of main observables such as temperature andmoisture from cloud and precipitation effects. Pre-viously, most cloud/precipitation affected radiancesare largely discarded in NWP data assimilationsystem as clouds and precipitation are discon-tinuous in time, space and their formation isassociated with complex, non-linear and not wellmodelled processes and also current data assimi-lation systems are constrained to using linearisedversions of these non-linear processes (Ohring andBauer 2011). Considering all these limitations,number of NWP centres make use of cloud-freemicrowave radiance observations by employing theapproach of clear sky simulations in which cloud-and precipitation-affected radiances are generallyscreened out in the quality control system based onbias calculated against the clear sky background.Assimilation of clear sky radiance data from differ-ent microwave satellite sensors has already shownpositive impact on forecast accuracy (Cameron et al.2005; Le Marshall et al. 2006; McNally et al. 2006).Better exploitation of cloud- or precipitation-

affected satellite measurements has great potentialfor further improvements of weather forecasting(Bauer et al. 2011). Assimilation of microwaveradiance data produces impact on deeper atmo-spheric moisture structures (Bauer et al. 2011)and is particularly important for accurate pre-diction of severe weather. The advantage ofmicrowave radiometer observations for nowcastingsevere convective activity over southeast India isreported in Madhulatha et al. (2013). Few effortshave been made for assimilating the cloud-affectedradiance measurements in microwave and infraredregions (Bauer et al. 2006, 2010, 2011; Geeret al. 2008). In European Centre for Medium-Range Weather Forecasts (ECMWF) assimilationof cloud- and precipitation-affected radiances fromdifferent satellite observations, viz., Special SensorMicrowave/Imager (SSM/I), Advanced MicrowaveScanning Radiometer (AMSR), Tropical MicrowaveImager (TMI) radiance observations (Bauer et al.2010; Geer et al. 2010) and Microwave Humi-dity Sensor (MHS) (Geer et al. 2014) have beenperformed.Direct assimilation of radiance data requires

relationship between model-state vectors and theobserved radiances as satellite radiances are not

components of atmospheric state vectors predictedby weather prediction models. Radiative transfermodels are utilised to transform the model fieldsinto radiance space, generally referred as forwardmodels. RTTOV (Radiative Transfer Model forTelevision Infrared Observation Satellite (TOVS)Operational Vertical sounder) is a rapid radiativetransfer model developed by EUMETSAT fundedNumerical Weather Prediction Satellite Applica-tion Facility (NWP-SAF). To simulate microwaveradiances scattered by cloud and precipitation,multiple scattering radiative transfer models arenecessary to include the scattering effects ofcloud hydrometeors. RTTOV-SCATT (RTTOV forscattering) is the scattering module within RTTOVto calculate the cloudy radiances in microwaveregions. For clear sky mode, RTTOV uses tem-perature, humidity and ozone profiles as input. Tocompute the cloud-affected radiances, additionalinputs of cloud hydrometeor profiles has to be sup-plied to report the scattering and emission effectsby hydrometeors (Weng and Liu 2003).SAPHIR is one of the four payloads of

Megha-Tropiques’ satellite. It is a microwaveradiometer with six channels operating at frequen-cies close to the water vapour absorption bandat 183.31 GHz (Eymard et al. 2001). The radi-ance observations from SAPHIR sensor provideshumidity information from surface to upper tropo-sphere under clear and cloudy weather conditions.Few attempts have been made to investigate theimpact of assimilation of cloud clear SAPHIR radi-ances into global models. Chambon et al. (2015)examined that the assimilation of SAPHIR radi-ances in the Meteo-France global model ARPEGEresulted in systematic improvements in the humid-ity distribution. Assimilation of SAPHIR radiancesinto WRF assimilation system showed consider-able improvements in the moisture analysis andsignificantly contributed to the prediction improve-ments (Singh et al. 2013). Ingestion of cloud clearSAPHIR radiances into NCMRWF GFS (GlobalForecast System) assimilation system has showedpositive impact in the prediction of relative humi-dity (Singh et al. 2015). Inclusion of clear-skySAPHIR radiances in the Met office Unified Modelimproved the analysis and forecasts (Rani et al.2015). So far, no efforts have been made to assim-ilate the cloud-affected SAPHIR radiances. Incor-porating cloud-affected SAPHIR radiance datainto assimilation system is very useful as this datacan provide moisture information in presence ofsevere weather system into the initial analysis of themodel. In the present paper, the possibility ofingestion of microwave cloud-affected SAPHIRradiances into global data assimilation system isinvestigated by using the forward operator multi-ple scattering radiative transfer models suitable for

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simulating the microwave cloud-affected radiances.The paper is structured as follows. Section 2presents the details about the Megha-TropiquesSAPHIR sensor. Description of radiative transfermodel and tropical cyclone case considered for sim-ulation are given in sections 3 and 4. Details of theNCMRWF Unified Model (NCUM) are specified insection 5. All-sky simulation results and compari-son with SAPHIR observations and various statis-tical analyses are presented in section 6 followed byconclusions in section 7.

2. Megha-Tropiques SAPHIR data

Megha-Tropiques (MT) is an Indo-French jointsatellite mission by ISRO, India and CNES, France.It is a low earth orbit satellite with circular orbitinclined 20◦ to the equator at an altitude of 866 km.Main objective of this mission is to understand thediurnal cycle of water vapour distribution, verticaltransports associated with the convective struc-tures. SAPHIR instrument is a part of MT pay-load to study the vertical distribution of watervapour in the tropical troposphere. MT SAPHIRmeasures radiation with cross-track scanning up toan incidence angle of 50◦ with a horizontal reso-lution of 10 km at nadir. It is a microwave scan-ning radiometer and provides measurements in sixwater vapour channels near absorption band ofwater vapour at 183.31GHz for sounding the atmo-spheric humidity. As moisture in the atmosphereattenuates microwave radiation emitted from thesurface of Earth, SAPHIR observations can pro-vide a detailed picture of atmospheric moistureat different channels. Six channels of SAPHIRmicrowave radiometer provide narrow weightingfunctions from surface to about 100 hPa and canretrieve water vapour profiles. Three channels outof six channels of SAPHIR are similar to thethree channels of AMSU-B and 2 channels of MHSonboard NOAA/METOP satellites and 5 channelsof ATMS onboard NPP satellite. Specifications ofSAPHIR sensor are given in table 1. More detailsof the sensor can be obtained from Mathur et al.(2013). In the present study, all-sky (clear andcloudy) simulation of microwave SAPHIR sensor

is performed using multiple scattering radiativetransfer model RTTOV-SCATT. Level 1 brightnesstemperature products from SAPHIR are utilisedfor validation purpose.

3. Observation operator RTTOV SCATTfor all-sky radiance simulation

Assimilation of satellite radiance observations intoNWP data assimilation system requires radiativetransfer models as observation operator. RTTOVis a fast radiative transfer model designed foruse in NWP data assimilation system for radi-ance simulations of passive infrared and microwavesounder/imager channels (Eyre 1991; Saunderset al. 2012). It simulates the satellite radiances (interms of black body equivalent brightness temper-ature, TB) for given atmospheric profile of temper-ature, moisture, variable gas concentrations, andcloud and surface properties at radiometer zenithangle. Parameters related to satellite position suchas latitude, longitude and azimuth angle also haveto be provided to the radiative transfer model.To simulate the cloud-affected radiances, scatter-ing effects of hydrometeors has to be accounted.RTTOV-SCATT is a multiple scattering radiativetransfer model which is a part of RTTOV packagefor simulating the microwave radiances in all-sky(clear sky and cloudy sky) conditions (Bauer et al.2006). In the present study, RTTOV-SCATT ofRTTOV9.3 package is utilised and more informa-tion on this model can be obtained from RTTOV-9User’s guide (https://nwpsaf.eu/deliverables/rtm/rttov9 files/users guide 9 v1.7.pdf).Liquid hydrometeors, viz., cloudwater, rain water

and frozen hydrometeors, viz., cloud ice and snowsrequired for RTTOV-SCATT are provided using24-hr forecasts from NCUM. The radiative transferequation is solved by computing the multiple scat-tering effects of hydrometeors at microwave fre-quencies using the Delta-Eddington approximation(Joseph et al. 1976; Moreau et al. 2003). Hydrome-teor optical properties (i.e., extinction coefficient,single scattering albedo, and asymmetry parameter)are provided to the radiative transfer model frompre-computed Mie tables for liquid water, cloud ice,

Table 1. Specifications of SAPHIR sensor.

Central Bandwidth Pressure levels

Channel frequency (MHz) Polarization (hPa)

S1 183.31±0.20 200 H ∼250–100

S2 183.31±1.10 350 H ∼400–250

S3 183.31±2.80 500 H ∼550–400

S4 183.31±4.20 700 H ∼700–550

S5 183.31±6.20 1200 H ∼850–700

S6 183.31±11.0 200 H ∼1000–850


Table 2. Input parameters necessary for RTTOV-SCATT.

Parameter Unit

Pressure hPaTemperature KSpecific humidity kg/kg to ppmvCloud coverCloud liquid water mixing ratio kg/kgCloud ice mixing ratio kg/kgRain flux kg/m2/sSnow flux kg/m2/sLand sea mask hPaSurface temperature KSurface pressure hPa2-m temperature K2-m specific humidity ppmv∗

10-m u wind component ms−1

10-m v wind component ms−1

Latitude DegreesLongitude DegreesZenith angle DegreesAzimuth angle DegreesSurface type Land/sea

∗Parts per million by volume.

rain and precipitating ice (Bauer et al. 2006).Transmittance for oxygen and water vapour arecomputed from regression tables driven by atmos-pheric predictors as in RTTOV package. Oceansurface emissivity is computed using FASTEM-2(Deblonde and English 2001) modules which requiresurface wind speed for its computations. Land sur-face emissivity comes from TELSEM atlas (Aireset al. 2011). More details on microwave radiativetransfer modeling in clouds and precipitation canbe found in Bauer (2002).RTTOV-SCATT can simulate radiances both

under clear and cloudy sky conditions. Clear skysimulations are performed by calling clear-skyRTTOV within RTTOV-SCATT module whichproduces brightness temperature of the clear skycolumn. Cloudy sky simulation is performed byRTTOV-SCATT which computes the cloud andrain-affected brightness temperatures. The all-skysimulated brightness temperature is computed asthe weighted average of brightness temperaturefrom clear and cloudy sky simulations. RTTOVmodel has forward, tangent and adjoint compo-nents. In the present study, forward model is onlyemployed as the main aim of the study is to simulatethe radiances of SAPHIR sensor in all-sky con-ditions. Input parameters necessary for RTTOV-SCATT simulation are summarised in table 2.

4. Description of the tropical cyclone‘Hudhud’

Tropical cyclone is typically associated with varioustypes of cloud structures and ideal to study the

importance of cloud-affected radiances. In the presentstudy, tropical cyclone ‘Hudhud’ which formed overthe Bay of Bengal is considered for simulation. Thecyclone initially formed as a low pressure systemon 6th October 2014 over Bay of Bengal. It subse-quently intensified into a depression on 7th October2014 over north Andaman Sea and adjoiningsoutheast Bay of Bengal and moved west–north-westwards towards Andhra Pradesh coast. Thedeep depression further intensified into cyclonicstorm Hudhud on 8th October 2014 and fur-ther moved west–north-westward and concentratedinto a severe cyclonic storm. The cyclone crossednorth Andhra Pradesh coast around Visakhapat-nam by forenoon of 12th October 2014. Subse-quently it weakened into a depression and laterdissipated. Complete details about the genesis anddissipation of the tropical cyclone ‘Hudhud’ can beavailable at http://www.rsmcnewdelhi.imd.gov.in/images/pdf/archive/bulletins/2013/RHUD.pdf.

5. Description of NCMRWF unifiedmodel (NCUM)

Profiles of temperature, specific humidity, cloudcover, liquid water, rain and precipitating snowand surface parameters are extracted from NCUMmodel forecasts. This global model is based on UKMet Office Unified Model version 7.9 with 4D-Vardata assimilation system. It is a non-hydrostaticmodel with deep atmosphere dynamics using asemi-implicit, semi-Lagrangian numerical scheme.The model includes a comprehensive set of parame-terisation, including surface, boundary layer, mixedphase cloud-microphysics and convection. It runson a latitude–longitude horizontal grid with ArakawaC staggering and a terrain following hybrid heightvertical coordinate with Charney–Phillip stagger-ing. Model configuration is provided in table 3.More details of NCUM model and its data assim-ilation system can be found in Rajagopal et al.(2012).NCUMpredictions which have produced reasonably

accurate forecast of location and intensity ofthe tropical cyclone Hudhud are utilised for the

Table 3. Configuration of NCUM.

Horizontal resolution 25 km

Model time step 10 min

Dynamics Terrain following hybrid height

coordinates

Time integration Semi-implicit

Radiation Edwards and Slingo (1996)

Boundary layer Lock et al. (2000)

Convection Gregory and Rowntree (1990)

Cloud micro-physics Wilson and Ballard (1999)


radiative transfer model simulations. In respectto different phases of cyclone, NCUM model runis carried out for 24 hrs based on 00 UTC initialconditions of 9–12 October, 2014. To assess theperformance of NCUM in capturing the rainfallassociated with different phases of the tropicalcyclone, the predicted rainfall is compared withobserved rainfall. For comparison IMD NCMRWFmerged satellite guage data (Mitra et al. 2009,2013) is utilised. This rainfall data is a mergedproduct of satellite estimates (TRMM) and rainguage observations (IMD) at 0.5◦ resolution, accu-mulated for 24 hrs. The observed rainfall (toppanel of figure 1) clearly shows the rainfall distri-bution associated with the movement of cycloneHudhud. NCUM model (bottom panel of figure 1)is able to capture the rainfall associated withdifferent phases of cyclone with slight variationsin spatial distribution and intensity of rainfall;however, model has underestimated the rainfallamounts when compared with observations.

6. Results and discussions

6.1 Simulation of all-sky and clear-sky radiancesfrom RTTOV-SCATT

Radiative transfer simulations are carried out forthe tropical cyclone Hudhud using NCUM modeloutputs. 24-hr forecasts of NCUM available at 3-hrinterval closer to the SAPHIR observations are con-sidered for RTTOV simulations. By supplying all

the necessary inputs, RTTOV simulation is per-formed and brightness temperatures are simulatedfor all the six channels of SAPHIR. Figure 2 showsthe comparison of simulated brightness temperatureboth in clear-sky (RTTOV clear sky simulationswithout including hydrometeor profiles) and all-sky(RTTOV-SCATT simulations, including hydrometeorprofiles) conditions along with SAPHIR observedbrightness temperature at 18 UTC of 11th October2014 for all the six channels. Associated withHudhud cyclone, SAPHIR observations (left panelof figure 2) clearly shows the cloud-affected radi-ances with lower values of brightness temperatures(TB) indicating the presence of deep convectiveclouds of cyclone. From different channels ofSAPHIR sensor (S6–S1), the signature of cycloneis clearly visible from surface to upper levels (leftpanel of figure 2) of atmosphere and is more promi-nent in lower troposphere peaking channel S6(1000–850 hPa). Lower brightness temperaturesassociated with the cloud structures of cyclone arenot reproduced in clear-sky simulations (mediumpanel of figure 2). All-sky simulations (right panel offigure 2) are able to produce the cloud-affected bri-ghtness temperatures associated with the cyclone.

6.2 Temporal evolution of horizontal distributionof radiance during different phases of cyclone

To understand the temporal evolution of horizontaldistribution of cloud-affected radiances duringthe cyclone movement, radiance simulations are

Figure 1. Observed and predicted 24 hr accumulated rainfall (mm) at 00 UTC during the life cycle of Hudhud. (a) 10thOctober, (b) 11th October, (c) 12th October, and (d) 13th October 2014.


Figure 2. Comparison of brightness temperatures (K) from SAPHIR observations and radiative transfer model (RTTOV)simulations under clear and all-sky conditions for Hudhud cyclone at 18 UTC of 11th October 2014 for all six channels(channels S1–S6 are given from bottom to top panels).

carried out during different phases of cyclone genesis,movement and landfall. As the high frequencychannel S6 (1000–850 hPa) is more sensitive tocloud and precipitation information, the horizontal

distribution for the channel S6 is illustrated infigure 3.Associated with the cloud structures during

different phases of cyclone, SAPHIR observations


Figure 3. Temporal evolution of horizontal distribution of brightness temperatures (K) during different phases of cyclonein channel S6 (183.31±11.0 GHz) from SAPHIR observations and RTTOV simulations.

(left panel of figure 3) clearly shows the signatureof cloud-affected radiances with low TB values inthe channel S6. Clear-sky simulations (mediumpanel of figure 3) are not able to produce thecloud-affected radiances. The horizontal distribu-tion of cloud-affected radiance provides moistureinformation in the cloudy regions which is wellsimulated in all-sky simulations (right panel offigure 3).

6.3 Vertical cross-section of tropical cyclonefrom brightness temperature

Intensity of tropical cyclone can be analysed byinvestigating the vertical cross-section of tropicalcyclone. Latitude/longitude height cross-sectionsof simulated and observed radiances valid for 18UTC of 11th October are shown in figure 4.SAPHIR observations (top panel of figure 4) clearlyrepresent the cloud-affected radiances related tothe vertical distribution of cloud which are wellsimulated in all-sky simulations (middle panel offigure 4) and are not simulated in clear-sky simula-tions (bottom panel of figure 4). The vertical cross-section of cloud-affected brightness temperature

in the vicinity of cyclone provides information onvertical distribution of moisture in the cloudyregions which is well captured in all-sky simulationsas in observations.

6.4 Three dimensional distribution of simulatedbrightness temperature in the vicinity of cyclone

To visualise the spatial (latitude, longitude andheight) distribution of cloud-affected radiances, thethree-dimensional distribution of simulated andobserved radiances is plotted. In all the six channelsof SAPHIR, cloud-affected brightness temperaturesassociated with the cloud structures embedded intropical cyclone is clearly observed (left panel offigure 5) which are not visible in clear sky simu-lations (middle panel of figure 5). All-sky simula-tions are able to produce the cloud-affected lowervalues of TB associated with the various cloudbands of cyclone (right panel of figure 5). Three-dimensional structures of cloud-affected radianceswhich provide moisture distribution in the vicinityof cyclone is well represented in all-sky simulations.This analysis suggests that the inclusion of scatter-ing processes from different hydrometeors in all-sky


Figure 4. Longitude-height and latitude-height cross sections of simulated brightness temperature (K) along with SAPHIRobservations at latitude 15◦ (left panel), at longitude 83◦E (right panel) for cyclonic storm Hudhud at 18 UTC of 20141011.

Figure 5. Three-dimensional distribution of brightness temperature (K) from SAPHIR observations and simulations at 18UTC of 11th October 2014.

simulations improved the simulation of cloud-affected radiances in all the channels.To evaluate the performance of the RTTOV-

SCATT in simulating the cloud-affected microwaveradiances of SAPHIR sensor, various statisticalanalyses are carried out by comparing the simulatedradiances against observed radiances. The region(10–20◦N; 60–120◦E) which covers the life cycleof Hudhud cyclone is considered for the statisticalanalyses.

6.5 Probability distribution functions (PDF)

To understand the distribution of simulated andobserved brightness temperatures, probability dis-tribution functions (PDFs) analysis has been per-formed. PDFs of simulated brightness temperature(TB) from clear-sky and all-sky simulations for var-ious channels along with SAPHIR observations areshown in figure 6. Cloud-affected lower TB val-ues in a particular channel indicate the presenceof cloud and high TB values generally correspondto cloud free regions. For upper troposphere peak-ing channels (S1–S3) (figure 6a, b, c), PDFs ofsimulated radiances from both clear-sky (RTTOV-Clear) and all-sky (RTTOV-All sky) simulations

(blue and black lines, respectively) are nearly equalwith some shift to lower TB values compared toobservations (red solid line). Generally, at upperlevels, the cloud content from different hydromete-ors at cloudy regions is less and hence, the impactof cloud scattering may be low at upper levelsand therefore, no significant differences are noticedbetween clear and all-sky simulated radiances forthe upper troposphere peaking channels (S1–S3).For the lower troposphere peaking channels

(S4–S6), PDFs of all-sky simulated radiances(black dotted line) shows a shift towards lower TB

values as in observations which are missing in clearsky (blue line) simulations (figure 6d, e, f). In all-sky simulations (black dotted line), the number ofoccurrences of cloud-affected lower TB values rang-ing from 240 to 260 K in the channels S4–S6 is moreand comparable to observations (red line). However,in clear-sky simulations (blue line), the frequencyof occurrence of cloud-affected lower TB values areless (figure 6d, e, f). From the PDF analysis it isclear that the lower tropospheric peaking channelsS4, S5 and S6 (figure 6d, e, f) which represents mid-dle to lower levels of the atmosphere are more sensi-tive to the cloud–radiation interaction processes ascloud amount is more concentrated at these levels.


At higher levels, the scattering associated withclouds may be low, thus the differences betweenboth the simulations are small (figure 6a, b, c)for channels (S1–S3). In general, these upper levelpeaking channels are strongly affected by scatteringfrom frozen particles in the upper parts of convec-tive systems (Hong et al. 2005). In simulation ofthe higher TB values in upper level channels, noconsiderable differences are noticed between boththe simulations. The reason may be due to under-estimation of NCUM simulations in estimating themass of frozen hydrometeors in the upper levels ofconvective clouds.Comparatively, all-sky simulations (black line)

are able to capture the cloud-affected lower val-ues of TB in all channels which are not producedin clear-sky simulations (blue line). Generally, thePDF distributions of all-sky and clear-sky simula-tions in simulating cloud-affected lower TB valuesare different due to the inclusion of scattering pro-cesses associated with cloud hydrometeors in all-sky simulations. In clear-sky simulations, TB valuesare more biased towards higher temperatures sinceradiative properties of clouds are not accountedin the simulations. Inclusion of hydrometers inthe cloudy simulations accounts for scattering byclouds at different layers, therefore all PDFs fromall-sky simulation (black dotted lines) shows a shifttowards the lower TB values which are closer toobservations indicating cloudier atmosphere asso-ciated with the cyclone structure. However, thedifferences between all-sky simulations against obser-vations can also be attributed to underestimationof different cloud hydrometeors from NCUM.

Improper estimation of temperature and moistureprofiles from NCUM could also result in differencesbetween simulations and observations.

6.6 Density scatter distribution

To understand the density distribution and correlationbetween simulated and observed TB values ofdifferent SAPHIR channels, density scatter plotsare prepared. Density scatter plots of all-sky andclear-sky simulated radiances against the observationsfor the upper troposphere channels (S1–S3) showno significant differences (left and middle panelsof figure 7). This indicates that distribution of TB

is more or less similar in both the simulations.All-sky and clear-sky simulations tend to pro-duce higher TB values compared to observations.For channel S3 (right panel of figure 7), all-skysimulation is able to produce lower TB valuesassociated with cloud as in observations which arenot simulated in clear-sky simulations.Density scatter plots for the lower troposphere

peaking channels S4–S6 are shown in figure 8. Highcorrelation is noticed between all-sky simulatedand observed radiances for lower troposphere peak-ing channels. In clear-sky simulation (bottom pan-els of figure 8), cloud-affected lower TB values arenot simulated; however, all-sky simulation (toppanels of figure 8) is able to produce the cloud-affected lower TB values in the range of 230–260 Kand TB values less than 230 K are not captured. Toquantify the strength of linear relationship betweenobservations and simulations, covariance is alsogiven in figures 7 and 8. Large values of covariance

Figure 6. Probability distribution functions of observed and computed SAPHIR TB in all channels during cycloneHudhud period.


Figure 7. Density scatter plots of observed and simulated TB (K) under clear and all-sky conditions for the considered daysduring cyclone Hudhud for channels S1–S3.

Figure 8. Density scatter plots of observed and simulated TB (K) under clear and all-sky conditions for the considered daysduring cyclone Hudhud for channels S4–S6.

are seen for all-sky compared to clear air in allchannels which suggests high correlation betweenall-sky simulations and observations, particularlyin simulating cloud-affected lower TB values. Thisclearly explains the greater sensitivity of scatter-ing process from cloud hydrometeors in realisticrepresentation of brightness temperatures.

6.7 Box plots

Box plot is a standardised way to display thedistribution of observed and simulated radiances in

terms of maximum, minimum, median and quartilevalues where the box represents 50% of data, 75%of data falls below the upper quartile and 25%falls below the lower quartile. The upper and lowerwhiskers give information about the values out-side the middle 50%. Figure 9 shows the box plotof brightness temperatures from clear and all-skysimulations along with observations. For channelS1 (figure 9a), 50% of data falls in the range of235–250 K for MT-SAPHIR, 230–245 K in all-skyand clear-sky simulations. Slight differences inmedian values and upper whisker values are reported


Figure 9. Bar plots of simulated and observed brightness temperatures.

between observations and simulations. However,large variations are seen in lower whisker values.Lower brightness temperature values (figure 9a)around 210 K are noticed in observations which arenot produced by both the simulations. For chan-nel S2, the distribution is confined to 245–260 Krange as in observations and for simulations thedistribution is between 240 and 260 K (figure 9b).Brightness temperatures on lower whisker side arenot produced in both the simulations. For channelS3, the distribution is in between 255 and 270 Kfor both observations and simulations (figure 9c).The lower whisker extends up to around 239 Kin all-sky simulations, but not in clear-sky simula-tions. In channel 4, the distribution of box is sim-ilar in observations and simulations whereas thedistribution corresponding to lower whisker is wellrepresented in all-sky simulations (figure 9d). Forchannels S5 and S6 (figure 9e, f), the distribution ismore or less similar in observations and simulationsfor upper whisker side; however, the distributionon lower whisker side is captured better in all-skysimulations which are not replicated in clear-skysimulations.From the present analysis it is clear that for the

upper level channels (S1, S2), the distribution ofsimulated brightness temperature is different fromobservations with large variations in lower whiskerside of TB and fewer differences in the upperwhisker side. Minimum and maximum values of TB

are not captured properly in both the simulationsfor upper level peaking channels. For the channelsS3–S6, the distribution of all sky-simulated bright-ness temperature is quite similar to observations;however, the distribution from clear-sky simulationis different from observations. Higher whisker sideof simulated brightness temperature correspondsto higher values of TB associated with clear-sky

Table 4. Maximum and minimum TB values from simula-tions and observations.

All-sky Clear MT-SAPHIR MT-All-sky MT-Clear

TMIN 212.69 268.33 146.09 –66.70 –122.25TMAX 294.41 294.31 291.89 –2.52 –2.43

conditions from both the simulations are quitesimilar to observations. Lower whisker side of bright-ness temperature corresponds to cloud-affectedlower TB values associated with cloudy scenes arebetter replicated in all-sky simulations but notin clear-sky. This analysis highlights the impor-tance of cloud hydrometeors in producing thecloud-affected brightness temperatures.The skill of all-sky and clear-sky simulations

in capturing higher and lower TB associated withclear and cloudy conditions is quantified in table 4.The minimum temperatures are reproduced bet-ter in all-sky simulations. Negative bias is noticedbetween observations and simulations which implythat simulated values are higher than observations.Even though all-sky simulations captured mini-mum TB values associated with the cloud, a differ-ence of 66.70 is seen which may be partly due to theunderestimation of cloud hydrometeors by NCUM.A bias of 122.24 is noticed in clear-sky simulation,since cloud-radiation interaction is not included inclear-air computation which might have resulted insimulating higher TB values. In case of maximumtemperature, both simulations have very less bias.The lowest TB values associated with cloud arenot fully estimated as in observations even in all-sky simulations. The performance of all-sky sim-ulations in simulating the magnitude of lower TB

values depends on the magnitude of hydrometeorssupplied from NCUM forecasts. Even thoughNCUM predicted the life cycle of Hudhud cyclone


reasonably well, the rainfall associated with thecyclone is underestimated when compared to rain-fall observations. Lower rainfall values implies lessamounts of hydrometeors and hence leads to lowerscattering effects from hydrometeors and affectthe respective radiative transfer computations andcontributes to the differences in simulating themagnitude of cloud-affected radiances when com-pared with observations particularly in lower levelpeaking channels.To understand the effectiveness of simulated

brightness temperature over observations in allchannels; correlation, bias and root mean squareerror (RMSE) between model simulated andobserved brightness temperature are calculated.From figure 10(a) it is clear that the bias (RTTOV-SAPHIR observations) is less in all-sky (red colour)compared to clear-sky (blue colour) simulationsfor all channels. Significant differences are noticedin bias values for the channels S4–S6 (figure 10a)which corresponds to middle to lower levels ofatmosphere and are much sensitive to cloud infor-mation. The bias in all-sky simulations (red colour)compared to clear-sky (blue colour) is less dueto inclusion of cloud hydrometeors. Positive biasindicates simulated TB values are higher comparedto observations which can be due to underesti-mation of NCUM model in producing the hydro-meteor profiles. For the channels S1 and S2 whichrepresents the higher levels of the atmosphere, neg-ative bias is observed for both simulations whichimplies RTTOV simulated TB values are lower thanobservations. These biases in simulated TB valuescan be attributed to errors in NCUM predictedatmospheric profiles. RMSE is also significantly

reduced in all sky simulations (figure 10b), partic-ularly for lower level peaking channels. High cor-relation is noticed in all-sky simulations for allchannels (figure 10c). All these statistical scoresreveal that all-sky simulated brightness tempera-ture in lower troposphere channels are closer toobservations and for the upper troposphere levelsthe behaviour of both the simulations is more orless similar.The possible sources of biases in the simulations

can be linked to errors in SAPHIR instrumentobservations, deficiencies related to model fore-cast fields and the radiative transfer models.As described in Buehler et al. (2004), SAPHIRobservations are affected by the limb effect: theTB values scanned at nadir are warmer in averageas compared to TB values scanned at the edge ofswath. This could be the reason for negative biasat upper level channels. However, understandingthe uncertainties in SAPHIR data is outside thefocus of present study. The underestimation ofcloud hydrometeors by NCUM can contribute tohigher values of TB which could be the cause forpositive bias at lower levels. Errors in RMSE mayarise due to two reasons: errors introduced by theradiative transfer simulations due to assumptionsin RTTOV and errors associated with the pre-dicted hydrometeor profiles. However, it is difficultto separate these two errors due to non-availabilityof measured hydrometeors profiles and simultane-ous measurements of brightness temperatures fromthe space borne radiometer. To incorporate thecloud-affected radiances in data assimilation sys-tem for real time purpose, one has to rely on modelforecasts only.

Figure 10. Bias, RMSE and correlation between RTTOV simulated and observed radiances for all channels in clear andall-sky conditions.


6.8 Role of hydrometeors on brightnesstemperature simulation

From different statistical analyses it is clear thatthe inclusion of scattering processes from differenthydrometeors is the reason for better simulationof cloud-affected radiances in all-sky simulations.Basically, radiance computations in RTTOV modelunder clear-sky conditions are based on atmosphericabsorption/emission and radiance simulations un-der cloudy conditions are based on Mie scatter-ing effects through different cloud hydrometeors. Inorder to investigate the sensitivity of hydrometeorson simulating cloud-affected lower TB values, cor-relation analysis is performed between simulatedTB at different channels and various hydrometeors.The effect of each of hydrometeors, namely cloud

water, cloud ice, rain and snow on simulated bright-ness temperature for all the six SAPHIR chan-nels, S1 (183.31±0.20GHz), S2 (183.31±1.10GHz),S3 (183.31±2.80GHz), S4 (183.31±4.20GHz), S5(183.31±6.20GHz) and S6 (183.31±11.0GHz) isdemonstrated in figure 11. To correlate the hydro-meteors with brightness temperatures at differentchannels, the hydrometeors present at the pressurelevels which correspond to the respective channelsweighing functions are only considered. For exam-ple, to compare the hydrometeor with S1 channel(250–100 hPa) brightness temperature, hydromete-ors between the same levels (250–100 hPa) are only

considered. Negative correlation is noticed betweensimulated TB and different hydrometeors.From figure 11(a), it is clear that the channels

S3 and S4 are more sensitive to cloud liquid watercompared to the rest of channels as the presenceof cloud liquid water is more expected between 700and 400 hPa levels which corresponds to S3 and S4channels. For cloud ice (figure 11b), the channelsS1–S4 are showing low correlation as cloud ice con-centration is dominant above 700 hPa to upper lev-els (100 hPa) of the atmosphere. No correlation canbe expected for the channels S5 and S6 as cloud icecannot be present at these levels (1000–700 hPa).For the case of rain hydrometeor (figure 11c), goodcorrelation exists for the channels S2–S6. Gener-ally, rain is concentrated more at the lower levels,so high correlation is noticed for the channel S6(1000–850 hPa) followed by the other channels. Asthe rain water concentration decreases from lowerto higher levels, correlation also follows the samepattern. No correlation is noticed for channel S1(250–100 hPa) as rain cannot be present at theselevels. In terms of snow (figure 11d), the channelsS1–S4 are sensitive. Good correlation is observedfor S1 (250–100 hPa) followed by the other chan-nels. As ice can be more abundant at higher lev-els and increase with height, more correlation isobserved at the higher levels. No correlation isobserved for channels S5–S6 as ice cannot form atlower levels (1000–700 hPa).

Figure 11. Effect of different cloudy hydrometeors on brightness temperature in different channels.


The above analysis clearly demonstrate thatthe high frequency channel S6 (183.31±11.0 GHz)corresponds to lower levels (1000–850 hPa) ofatmosphere is most sensitive to rain (figure 11c)compared to all the other channels followed by S5and S4. The frequency channel 183.31±0.2 GHz(S1) is most sensitive to cloud ice followed byS2, S3 and S4 channels (figure 11b). The channelS4 (183.31±4.2GHz) shows highest sensitivity tocloud water (figure 11a) followed by channel S3(183.31±2.8 GHz). The present results are con-sistent with the study by Hong et al. (2005) inwhich the sensitivity of brightness temperatureat microwave frequencies to hydrometeors in deepconvective clouds is clearly discussed. Negative cor-relation is observed between different hydrometersand TB in respective channels. In all the channels,brightness temperatures are on lower side withincrease in hydrometeor content. All consideredcloud hydrometeors (cloud ice, cloud water, snow,rain) contribute to the decrease of TB in channelsas scattering effects from different hydrometeorsresults in redistribution of radiation. This clearlyreveals the importance of hydrometeors in simu-lating the cloud-affected brightness temperaturesand highlights the importance of all-sky radiancesimulations. The significance of optical propertiesof hydrometeors in all-sky radiance simulation isdiscussed in detail in Mahfouf et al. (2016).

7. Conclusions

In the present paper, the possibility of simulationof cloud- and precipitation-affected radiances isinvestigated by performing all-sky radiance simula-tions of MT-SAPHIR sensor using multiple scatter-ing radiative transfer models (RTTOV-SCATT).NCUM predicted profiles of temperature, moistureand hydrometeor profiles during different phasesof tropical cyclone Hudhud are used as input toRTTOV-SCATT to generate observation equivalentradiances for SAPHIR sensor. Simulated radiancesunder clear-sky and all-sky conditions are compa-red with SAPHIR observations. The three dimen-sional distribution of cloud-affected radiance in thevicinity of cyclone is better captured in all-skysimulations.Various statistical analyses are carried out to

study the capability of all-sky simulations overclear-sky simulations by comparing with SAPHIRobservations. PDF analysis revealed that the dis-tribution of all-sky simulations are shifted moretowards lower TB values as in observations whichcorrespond to cloudy sky conditions; however, thedistribution of clear-sky simulations are shiftedmore towards higher TB values which representsclear-sky conditions. Scatter density plots revealed

that all-sky simulated radiances are more consistentwith observations and bar plot analysis showedthe ability of all-sky simulations in capturingthe cloud-affected lower TB values. Area averagedstatistics carried out by considering different daysof cyclone revealed that the bias and RMSE of radi-ances are reduced in all-sky simulations. Improvedcorrelation against observations is evident in all-sky simulations for all channels. The overalldistribution of cloud-affected radiances is wellcaptured in all-sky simulations compared to clear-sky simulations. The role of cloud hydrometeorsin simulating cloud-affected brightness tempera-tures in all-sky simulations is also investigated.Negative correlation is noticed between hydro-meteors and brightness temperatures. This impliesthat the presence of cloud hydrometeors lowers theTB values due to redistribution of radiation due tomultiple scattering processes in presence of clouds.The analysis suggested that inclusion of scatteringeffects of cloud hydrometeors improved the simu-lation of cloud-affected lower TB values in all-skysimulations.Even though all-sky simulations are able to

produce the cloud-affected brightness temperatures,magnitude of simulated cloud-affected brightnesstemperature are significantly different from obser-vations. The biases and errors in simulations cor-respond to various assumptions in RTTOV model,improper estimation of hydrometeors and vari-ous atmospheric profiles by atmospheric predictionmodels and also errors related to sensor. A reviewof sources of systematic errors and uncertaintiesin observations and simulations at microwave fre-quencies are discussed in Brogniez et al. (2016).In the present study RTTOV-SCATT v9.3 isutilised, however use of later versions can signifi-cantly upgrade the quality of all-sky simulations asdiscussed in Geer and Baordo (2014).To reduce these biases in RTTOV simulations,

improvements in observation operators in terms oftreatment of sub-grid scale cloud variability andbetter parameterisations of particle single scat-tering properties is very much essential. There isa need for improved prediction of hydrometeorsby numerical models by proper representation ofphysical processes associated with convection andcloud microphysics and also instrument biasesrelated to sensor has to be minimised. Even withall these limitations, the three-dimensional struc-ture of cloud-affected radiances which provide thehumidity information in the vicinity of tropi-cal cyclone Hudhud is well represented in all-skysimulations of RTTOV-SCATT. Assimilation ofthese cloud-affected radiances into the model’s ini-tial conditions can provide the moisture informa-tion in cloudy regions. To understand the impactof cloud-affected radiance in the initial analysis


of model, assimilation has to be performed whichis aimed in our future study. The present studythus highlights the importance of implementationof RTTOV-SCATT observation operator in NCUMdata assimilation system to assimilate the cloud-affected SAPHIR microwave radiances.

Acknowledgements

We would like to thank Dr Roger Saunders, MetOffice, UK, for providing valuable inputs related toRTTOV-SCATT. NWP SAF user’s page is greatlyacknowledged for detailed description on applica-tion of RTTOV. Sincere thanks to NCAS-CMS andauthors of NDdiag UM Tool. Special thanks to DrSwapan Mallick, Kyungpook National Universityfor technical discussions. We acknowledge MOS-DAC/ISRO for making SAPHIR data available.We thank two anonymous reviewers and editor fortheir helpful suggestions.

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MS received 7 June 2016; revised 4 September 2016; accepted 19 October 2016

Corresponding editor: Ashok Karumuri

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