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RTTOV 10 Theory & Exercise

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1 RTTOV 10 Theory & Exercise
Overview of RTTOVS Radiative Transfer Model Gases Absorption (water Vapor & other trace gases) Aerosol/Clouds Scattering and Absorption Surface Emissivity/Reflectivity IRSSE & ISEM-6 Model Example An Exercise in Radiative Transfer Modeling with RTTOV-10 (RTTOV10_Workshopv4.doc; prepared by Dr. Nadia Smith) Running RTTOV-10 using VMware Player RTTOV-10 in IMAPP Virtual Appliance (VA) (prepared by Willem Marais) Allen Huang, SSEC/CIMSS University of Wisconsin-Madison, USA IMAPP Training Workshop 2011 Satellite Direct Broadcast for Real-Time Environmental Applications ECNU Shanghai, 1-6 June 2011

2 General Notes BTs have been calculated by RTTOV v9.3
Initial settings: over sea water, no solar radiation ISEM is RTTOV’s fast surface emissivity s routine for the IR to compute surface emissivity over water SST=surface air temperature (last level in model atmosphere profile)

3 Polar Orbiting: Geostationary: RTTOVS – Hyper and Multi –Spectral RTM
Satellite Platforms supported in RTTOV Polar Orbiting: NOAA DMSP TRMM ERS EOS METOP ENVISAT FY-1 ADEOS CORIOLIS Geostationary: Meteosat GOES GMS FY-2 MSG FY-1 MTSAT To request a copy of the RTTOV9.1 code go to User’s Guide (distributed as part of lecture’s materials): RTTOVS users_guide_92/pdf

4 Microwave: Infrared/Near IR/Visible:
RTTOVS – Hyper and Multi –Spectral RTM Satellite Sensors supported in RTTOV Infrared/Near IR/Visible: HIRS AVHRR VTPR1 & VTPR2 AIRS MODIS ATSR IASI MVIRI SEVIRI GOES-Imager & Sounder GMS/MTSAT imager FY2-VISSR FY1-MVISR CriS VIIRS Microwave: MSU SSU AMSU-A AMSU-B SSMI TMI SSMIS HSB MHS AMSR WINDSAT

5 RTTOVS – Hyper and Multi –Spectral RTM
Atmospheric Radiative Transfer Approximation (1/2) Simulation of radiances reaching satellite sensor The model can simulate both clear sky radiances and cloudy radiances. It uses an approximate form of the atmospheric radiative transfer (RT) equation. If a black opaque cloud is assumed at a single level, the top of the atmosphere upwelling radiance, at a frequency  and viewing  angle from zenith at the surface, neglecting scattering effects, is written as: Where are the clear sky and fully cloudy top of atmosphere upwelling radiances and N is the fractional cloud cover.

6 RTTOVS – Hyper and Multi –Spectral RTM
Atmospheric Radiative Transfer Approximation (2/2) Simulation of clear air radiances If N, the cloud cover parameter, is set to zero and the liquid water concentration profile vector is set to zero both the infrared and microwave radiances computed are for clear air with the second right hand term of equation in previous slide (7) being zero. can be written as: where s is the surface to space transmittance, s is the surface emissivity and B(,T) is the Planck function for a frequency  and temperature T. The transmittances, , are computed by means of a linear regression in optical depth based on variables from the input profile vector.

7 RTTOVS – Hyper and Multi –Spectral RTM
Infrared Surface Emissivity Model (ISEM) (1/2) To compute IR s over water there are fast IR surface emissivity routine. This model all compute a surface emissivity for the channel of interest at the given viewing angle. Over the land and sea-ice surfaces only approximate default values are provided for the surface emissivity in the infrared (see Table above). The user also has the option of providing their own estimate of surface emissivity to the model if desired. ISEM-6: Sherlock, V ISEM-6: Infrared Surface Emissivity Model for RTTOV-6. NWP SAF report.

8 RTTOVS – Hyper and Multi –Spectral RTM
Infrared Surface Emissivity Model (ISEM) (2/2) ISEM-6: Sherlock, V ISEM-6: Infrared Surface Emissivity Model for RTTOV-6. NWP SAF report m Emissivity

9 InfraRed Sea Surface Emissivity (IRSSE) model
(1/2) The InfraRed Sea Surface Emissivity (IRSSE) model was developed for use in the Global Data Assimilation System (GDAS) at NCEP/EMC. Previously, the GDAS used an IRSSE model based on Masuda et al (1988). The Masuda model doesn't account for the effect of enhanced emission due to reflection from the sea surface (only an issue for larger view angles) and the implementation was based on coarse spectral resolution emissivity data making its application to high resolution instruments, such as AIRS, problematic. The old IRSSE model has been upgraded to use sea surface emissivities derived via the Wu and Smith (1997) methodology as described in Van Delst and Wu (2000). The emissivity spectra are computed assuming the infrared sensors are not polarised and using the data of Hale and Querry (1973) for the refractive index of water, Regenstein (1981) for the extinction coefficient, and Friedman (1969) for the salinitiy/chlorinity corrections. Instrument spectral response functions (SRFs) are used to reduce the emissivity spectra to instrument resolution.

10 InfraRed Sea Surface Emissivity (IRSSE) model
(2/2) Methodology A starting point was the sea surface infrared emissivity model, SSIREM, described in Sherlock (1999),                                              Where is nomarlized view angle and  is the wind speed in m/s. In generating the model coefficients, for a series of wind speeds 0-15m/s, the coefficients Ci were obtained using Levenberg-Marquardt least-squares minimization. Interpolating coefficients for each Ci as a function of wind speed were then determined. In using the model, the Ci are computed for a given wind speed and these computed coefficients are used in equation above to calculate the view angle dependent emissivity. Van Delst, P.F.W. and Wu, X. A high resolution infrared sea surface emissivity database for satellite applications. Technical Proceedings of The Eleventh International ATOVS Study Conference, Budapest, Hungary September 2000,

11 RTTOVS – Hyperspectral RTM Absorption Gases’ Options
To effectively simulate effects of gases’ absorption in the window channel measurements and their impact on SST retrieval

12 RTTOVS – Hyperspectral RTM Clouds and Aerosols Absorption/Scattering
The mixing of the various aerosol components can be defined by the user or climatological profiles with predefined mixing can be supplied. The input profile. To include an aerosol component in the radiative transfer the user must assign the layer mean density (in units of cm-3) for that component.

13 RTTOVS Calculations with s=1 and s from ISEM

14 RTTOVS Calculations with s=1 IASI and MODIS BTs for 5 atmospheres
Explain why window channels not have the same value? Explain why for some IASI & MODIS ch. have large differences than others?

15 IASI and MODIS BTs vs. SST for 5 channels
RTTOVS Calculations with s=1 IASI and MODIS BTs vs. SST for 5 channels Explain why some window Chs have temp. warmr than SST? Explain how water vapor play the role in retrieving SST?

16 SST vs. TPW for 5 atmospheres, s=1
Discuss any relation between SST and TPW

17 IASI and MODIS BTs for 5 atmospheres
RTTOVS Calculations with s from ISEM IASI and MODIS BTs for 5 atmospheres Explain why window channels not have the same value? Explain why for same ch. IASI & MODIS have large difference? Are there any difference when compare with s is 1?

18 IASI and MODIS BTs vs. SST for 5 channels
RTTOVS Calculations with s from ISEM IASI and MODIS BTs vs. SST for 5 channels Explain why some window Chs have temp. warmr than SST? Explain how water vapor play the role in retrieving SST?

19 IASI and MODIS BT differences “s=1 minus s from ISEM”
RTTOVS Calculations with s=1 & s from ISEM IASI and MODIS BT differences “s=1 minus s from ISEM” Are these differences always positive?

20 RTTOVS Calculations with and without H2O

21 RTTOVS Calculations with and without H2O
IASI and MODIS BTs vs. TPW for 5 channels, s from ISEM Discuss the relationship between brightness temperatures and TPW!

22 RTTOVS Calculations without H2O & s from ISEM
IASI and MODIS BTs vs. TPW for 5 channels Discuss the relationship between brightness temperatures and TPW! Also discuss the differences of IASI and MODIS of their water vapor impacts Tropics SST[K] TPW[cm] Ch# WL[m] IASI[K] MODIS[K] IASI Diff[K] MODIS Diff[K]

23 RTTOVS Calculations without H2O & s from ISEM
IASI and MODIS BTs vs. TPW for 5 channels MID LATITUDE WINTER SST[K] TPW[cm] Ch# WL[m] IASI[K] MODIS[K] IASI Diff[K] MODIS Diff[K] Explain why water vapor affects much differently to IASI and MODIS when TPW is high! MID LATITUDE SUMMER SST[K] TPW[cm] Ch# WL[m] IASI[K] MODIS[K] IASI Diff[K] MODIS Diff[K]

24 RTTOVS Calculations without H2O & s from ISEM
IASI and MODIS BTs vs. TPW for 5 channels SUBARCTIC SUMMER SST[K] TPW[cm] Ch# WL[m] IASI[K] MODIS[K] IASI Diff[K] MODIS Diff[K] Explain why water vapor affects much differently to IASI and MODIS when TPW is high! SUBARCTIC WINTER SST[K] TPW[cm] Ch# WL[m] IASI[K] MODIS[K] IASI Diff[K] MODIS Diff[K]

25 Simple Cloud calculations
for different cloud top pressures (ctp) and cloud fractions (cfrac)

26 IASI and MODIS BTs, CTP=500 mbar, CFRAC=0.1
RTTOVS Calculations with s from ISEM IASI and MODIS BTs, CTP=500 mbar, CFRAC=0.1

27 Exercise 1: Find the relationship between Brightness Temperatures and TPW
# MID LATITUDE SUMMER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # MID LATITUDE WINTER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # SUBARCTIC SUMMER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # SUBARCTIC WINTER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K]

28 Exercise 2: Find the relationship between SST & Brightness Temperatures
# MID LATITUDE SUMMER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # MID LATITUDE WINTER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # SUBARCTIC SUMMER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K] # SUBARCTIC WINTER SST[K] TPW[cm] WL[m] IASI BT[K] MODIS BT[K]

29 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

30 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

31 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

32 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

33 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

34 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

35 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | | |

36 IASI BTs for cloudy cases, various cloud heights & fractions
RTTOVS Calculations with s from ISEM IASI BTs for cloudy cases, various cloud heights & fractions

37 MODIS BTs for cloudy cases, various cloud heights & fractions
RTTOVS Calculations with s from ISEM MODIS BTs for cloudy cases, various cloud heights & fractions

38 Aerosol calculations (using climatological profiles)

39 IASI and MODIS BTs, Aerosol climatology: continental clean
RTTOVS Calculations with s from ISEM IASI and MODIS BTs, Aerosol climatology: continental clean

40 IASI and MODIS BTs, Aerosol climatology: urban
RTTOVS Calculations with s from ISEM IASI and MODIS BTs, Aerosol climatology: urban

41 IASI and MODIS BTs, Aerosol climatology: desert
RTTOVS Calculations with s from ISEM IASI and MODIS BTs, Aerosol climatology: desert

42 IASI and MODIS BTs, Aerosol climatology: maritime clean
RTTOVS Calculations with s from ISEM IASI and MODIS BTs, Aerosol climatology: maritime clean

43 IASI BTs for different aerosol climatologies
RTTOVS Calculations with s from ISEM IASI BTs for different aerosol climatologies

44 MODIS BTs for different aerosol climatologies
RTTOVS Calculations with s from ISEM MODIS BTs for different aerosol climatologies Discuss aerosol impatcs on the brightness temp. Also compare IASI and MODIS to see which measurements suffer more impacts.

45 IASI BT differences “No aerosol minus aerosol climatology”
RTTOVS Calculations with s from ISEM IASI BT differences “No aerosol minus aerosol climatology”

46 MODIS BT differences “No aerosol minus aerosol climatology”
RTTOVS Calculations with s from ISEM MODIS BT differences “No aerosol minus aerosol climatology”

47 Double trace gas concentrations (O3 only)

48 IASI and MODIS BTs with original Ozone
RTTOVS Calculations with s from ISEM IASI and MODIS BTs with original Ozone

49 IASI and MODIS BTs for doubled Ozone
RTTOVS Calculations with s from ISEM IASI and MODIS BTs for doubled Ozone

50 IASI BTs with and without doubled ozone
RTTOVS Calculations with s from ISEM IASI BTs with and without doubled ozone Why we need to have good estimate of CO2 concentration for SST retrieval?

51 MODIS BTs with and without doubled ozone
RTTOVS Calculations with s from ISEM MODIS BTs with and without doubled ozone Why we need to have good estimate of CO2 concentration for SST retrieval? Who suffer more? IASI or MODIS?

52 Impact of CO2 Concentration
BT with 360 ppmv minus BT with 340,345,…380 ppmv

53 Impact of CO2 Concentration
Mean & RMS of [True – ppmv] Mean Error RMS Error TSkin [K] TSkin [K] ‘True’ CO2 amount: 375 ppmv

54 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

55 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

56 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

57 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

58 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

59 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | |

60 Radiance Table for MODIS Window Channels
Temp.(K) | | | | | | | | | | | | | | | | | | | | |

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