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Atmospheric Humidity Profile Ret

VIEWS: 20 PAGES: 39

									Atmospheric Humidity Profile Retrieval Algorithms for
             Megha-Tropiques SAPHIR:
   A Simulation Study & Analysis of AMSU-B Data



            B.S. Gohil and A.K. Mathur

            Oceanic Sciences Division
       Meteorology & Oceanography Group
        Space Applications Centre (ISRO)
           Ahmedabad – 380015, INDIA
           SAPHIR & AMSU-B CHANNEL SPECIFICATIONS


Channel       SAPHIR Central    #SAPHIR Freq.    *AMSU-B Freq.
  No.           Freq. (GHz)      used (GHz)         (GHz)
    1          183.310.15          183.16               ----
    2          183.311.20          182.11         182.31 (B5)
    3          183.312.80          180.51         180.31 (B4)
    4          183.314.30          179.01               ----
    5          183.316.60          176.71         176.31 (B3)
    6          183.3111.00         172.31               ----
                                                *Other channels
#Earlierversion
                                                are 89 & 150 GHz
Latest specifications have minor deviations
EMISSION OF MICROWAVE RADIATION
                               RADIATIVE TRANSFER MODEL

 Total brightness temperature radiated by a non-scattering earth-
 atmospheric system under LTE


                                                                                             
TB( ,  , p)  Ts. ( ,  , p). ( ,  )  TBup( ,  )  TBdn(( ,  ).{   ( ,  , p)}. ( ,  )
                                                                            1
                                     
 ( ,  )  exp{ sec    ( , z )z}
                                      0
                                                                     = atmospheric absorption
TBdn( ,  )  sec  T ( z ). ( , z ). ( ,  ,0  z )z          = surface emissivity
                         0
                                                                      = atm. transmittance
                         
TBup( ,  )  sec  T ( z ). ( , z ). ( ,  , z  )z          = incidence angle
                         0                                            = frequency
                                          z2
 ( ,  , z1  z 2)  exp{ sec   ( , z )z}                    Tbup = up-welling BT
                                          z1                         TBdn = down-welling BT
                                                                     Ts = surface temperature
                                                                     p = polarization
                    RADIATIVE TRANSFER MODEL (CONT…)

At microwave sounding channels, total transmittance is mostly negligible
yielding total brightness temperature as


TB( ,  )  TBup( ,  )
                       
TBup( ,  )  sec  T ( z ). ( , z ). ( ,  , z  )z
                       0
                       
TBup( ,  )  sec  T ( z ).w( ,  , z ).z
                       0

w( ,  , z )   ( , z ). ( ,  , z  )
                   c   / 2                         w=weighting function
 w( ,  , z )         w( f ,  , z )f
                   c   / 2
                                                       =channel bandwidth

                                                       c=channel central freq.
          SAPHIR CHANNELS’ RESPONSE (NADIR VIEW)




       Dry Atmosphere                  Moist Atmosphere

At nadir view with dry atmosphere, the low freq channels are
contaminated by surface contributions
          SAPHIR CHANNELS’ RESPONSE (OBLIQUE VIEW)




         Dry Atmosphere                       Moist Atmosphere

At oblique view with moist atmosphere, the low freq channels are
less sensitive to boundary layer humidity which contributes the most
to many meteorological & oceanographic processes
      Status/Conclusions of Previous Studies…(1)


• Studied the impact of humidity, temperature, observational
 geometry & frequency of SAPHIR on weighting functions
• Large changes in altitude/pressure of weighting function
 peaks due to changes in humidity & temperature suggest
 that it is better to infer layer integrated moisture than the
 level moisture
• Effective thickness of layers must be chosen by
 considering the largest variation in the weighting function
 peak altitudes of channels
     Status/Conclusions of Previous Studies….(2)

• Developed algorithms for retrieving humidity profiles over
 land & ocean using layer integrated moisture from SAPHIR
 through simulations for clear-sky conditions
• Combined use of layer water vapour from SAPHIR with
 total water vapour from MADRAS improves low level
 humidity profile over oceans
• Use of EOF technique for retrieving low level humidity
 profile specifically over land is suggested in view of non-
 availability of total water content from MADRAS over land
 and due to surface contaminations in low-freq channels
    SIMULATION STUDIES &
RETRIEVALS FROM NOAA-AMSU/B
RELATIVE HUMIDITY PROFILES OF VAISALA RADIOSONDE DATA



                                         Data depicts large
                                         variations in profiles
                                         of relative humidity
MODEL USED FOR SIMULATING RELATIVE HUMIDITY PROFILES



                        RELATIVE HUMIDITY VARIATIONS
 ALTITUDE
                                                  INTERVAL (%)
               MINIMUM (%)      MAXIMUM (%)
                                                    OR CASES

 SURFACE           10                80                10%

LOWER LAYER      -80% OF          +80% OF         5 CASES FOR
  AT 4 KM     SURFACE VALUE    SURFACE VALUE      TOTAL RANGE
UPPER LAYER   -80% OF LOWER    +80% OF LOWER      5 CASES FOR
  AT 8 KM       LAYER VALUE     LAYER VALUE       TOTAL RANGE
TROPOPAUSE
 (TROPICAL)         5                10                5%
   (16 KM)
 TOP OF THE
                              FIXED VALUE (0 %)
ATMOSPHERE
STATISTICS OF SIMULATED TROPICAL ATMOSPHERES

    Parameter    Min     Max     Mean    SD
     SST (K)     285.0   305.0   296.1   6.73
    WV (g/cm2)   1.00    6.82    2.60    1.33
     SW (m/s)     3.0     6.0    4.45    1.50
AMSU-B CHANNELS’ WEIGHTING FUNCTION PEAK VARIATIONS



        CHANNEL
                     LOWER LIMIT     UPPER LIMIT
       FREQUENCY
                        (hpa)           (hpa)
          (GHz)

         176.31         1000            500
         180.31         900             400
         182.31         750             300
                   Clear sky cases
RT SIMULATION BASED SELECTION OF LAYERS
    USED FOR RETRIEVALS FROM AMSU-B


PRESSURE   SL1/L1   L2     L3      L4
  1000
  900
  800
  750
  700
  600
  500
  400
  300
  200
  100
STATISTICS OF HUMIDITY FOR SELECTED LAYERS

   Parameter      Min     Max     Mean     SD
   LARH-1(%)
                  13.75   80.00   48.66   16.72
 (1000-500 hpa)
  LARH-2 (%)
                  12.33   80.00   46.02   16.82
 (900-400 hpa)
  LARH-3 (%)
                  10.50   79.25   41.85   17.83
 (750-300 hpa)
  LARH-4 (%)
                  9.57    59.55   29.27   13.22
 (500-100 hpa)
    Sensitivity of AMSU-B Channel to Humidity in Different Forms




•BT has higher correlation with Layer-WVC at constant temperature as
 compared to combined Layer-WVC
•BT better correlated to Layer Average RH as compared to Layer-WVC
           Dual Nature of Low Frequency AMSU-B Channel
              under Different Atmospheric Conditions


Layer 900 to 700 hpa

                               285K               290K
Under cold conditions, 176
 GHz channel behaves
•Like radiometer indicating
 increase in BT with initial
 small increase in RH
 indicated by +ve slope
•Like sounder depicting
 decrease in BT with                                     ALL
 further increase in RH                305K
 depicted by –ve slope
This has impact on
 humidity retrievals
      SENSITIVITY OF BT ON LAYER AVERAGED RH WITH
                   VARIABLE THICKNESS

Sub layer-1: 900 to 700 hpa          Layer-1: 1000 to 500 hpa




   GIVEN RANGE OF BT YIELDS WIDER RANGE OF RH FOR THICKER LAYER
Response of Mid-Frequency AMSU-B Channel to Cold Atmospheres




Layer 900 to 700 hpa
                                    285K                 290K

180 GHz channel does
not show duality even
under cold & dry
conditions



                                    305K                 ALL
Response of AMSU-B Channels to Different Layers
IMPACT OF DRY CONDITIONS ON HUMIDITY RETRIEVAL

     LAYER        CORRELATION COEFF. (%)        MULTI.       RMS
   AVERAGED                                     CORR.       ERROR
     RH (%)      176 GHz   180 GHz   182 GHz     (%)       loge(RH)

    1000-500      -37.4     -80.2      -71.2     84.6       0.252
      hpa         -79.1     -79.5      -65.8     82.6       0.219

     900-400      -42.5     -87.8      -82.4     90.5       0.206
       hpa        -82.4     -88.8      -79.3     89.1       0.186

     750-300      -48.5     -93.4      -93.3     96.1       0.147
       hpa        -80.3     -94.0      -92.0     95.1       0.149

     500-100      -47.2     -86.1      -95.5     95.6       0.140
       hpa        -67.0     -85.8      -95.9     96.3       0.127
   VALUES FOR FULL WVC RANGE          VALUES FOR WVC >1.0 g/cm2

   Total WVC data useful in better retrievals
   Use of separate algorithm for cold-dry conditions suggested
Response of AMSU-B Channels to Humidity in Selected Layers


            CH2             CH1
                                             • BT & RH
         CH3                                   dependency in
                                               general is
                                               logarithmic
                                             • Atmospheres
                                               with WVC >1
                                               g/cm2 used for
                                               algorithm
      CH3                                      development


                                    CH1
       HUMIDITY PROFILE RETRIEVAL MODEL



                  exp{A
                                              iN
       LARHp                       0, p      A
                                              i 1
                                                        .TBi
                                                     i, p   }
Where
LARH = Layer averaged relative humidity in percent
A0,p    = retrieval constant for pth layer
Ai,p    = retrieval coefficient for ith channel
TBi     = Brightness temperatures of ith AMSU-B channel
N       = Total number of AMSU-B channels used (N=3)
           HUMIDITY RETRIEVAL FROM AMSU-B DATA

DATA:
        NOAA-16 AMSU-B BT data
REGION:
        Indian Region (45° E to 115° E Lon, 0° to 50° N Lat)
PERIOD:
        A) June 9, 2002 at 7 & 19 GMT
        B) October 22, 2002 at 7 & 20 GMT
LAYER AVERAGED RELATIVE HUMIDITY:
        1) 1000-500 hpa
        2) 900-400 hpa
        3) 750-300 hpa
INTERCOMPARISON:
        NOAA CIRES Climate Diagnostics Center Data of RH
        At 2.5° Lon-Lat Grid within 2 hours
Comparison of Derived Humidity with Model
               090607r1
Comparison of Derived Humidity with Model
               090607r2
Comparison of Derived Humidity with Model
               090607r3
Comparison of Derived Humidity with Model
               090619r1
Comparison of Derived Humidity with Model
               090619r2
Comparison of Derived Humidity with Model
               090619r3
Comparison of Derived Humidity with Model
               221007r1
Comparison of Derived Humidity with Model
               221007r2
Comparison of Derived Humidity with Model
               221007r3
Comparison of Derived Humidity with Model
               221020r1
Comparison of Derived Humidity with Model
               221020r2
Comparison of Derived Humidity with Model
               221020r3
                          CONCLUSIONS

Results of the present preliminary study indicate that:
• Layer averaged RH is better represented by BT as compared to
  Layer WVC
• Due to wide & overlapping weighting functions of humidity
  sounding channels, retrievals using multi-channel data have been
  performed
• Low absorbing channel depicts dual dependency on humidity
  under cold conditions suggesting the need of moisture/
  temperature dependent retrieval algorithms
• Total moisture either to be derived from humidity channels or to be
  supplemented externally (from imager data over ocean & land-?)
• Algorithms for deriving level RH from layer averaged RH to be
  developed

Detailed studies are in progress for operationalization.

								
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