Radiometric Inter-calibration of SAPHIR using the Microwave Humidity Sounders

Hamideh Ebrahimi, Student Member, IEEE, Saswati Datta, Member, IEEE, Andrea Santos-Garcia, Student Member, IEEE, and

Linwood Jones, Life Fellow, IEEE

Abstract— The Microwave Humidity Sounders (MHS) on NOAA and Metop satellites have been extensively inter-satellite cross-calibrated; thus these MHS radiometers serve as the radiometric calibration standards for other satellite sounder radiometers. Unfortunately, inter-satellite radiometric calibration (aka XCAL) between similar, but not identical, radiometers is not trivial. This paper explores the application of a “Double-Difference (DD)” XCAL technique to sounders, which has been successfully used for conical scanning microwave imagers. Specifically, the water vapor sounder Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie (SAPHIR) on the Megha-Tropiques satellite is inter-satellite calibrated using the MHS instruments on the NOAA-18, NOAA-19, and Metop-A satellites.

Keywords—SAPHIR, MHS, GMI, NOAA, Metop

I. INTRODUCTION The Global Precipitation Measuring (GPM) mission is an

international effort to measure rain and snow worldwide every 3 hours. This data eventually will improve our understanding of water cycle and climate change.

The GPM Microwave Imager (GMI) is a 13 channel microwave radiometer, ranging from 10 to 183 GHz and is designed to be a calibration transfer standard (reference) to other constellation members’ passive microwave instruments (targets), for the inter satellite-calibration process. GMI has two water vapor sounding channels, 183±3 GHz and 183±7 GHz, that are going to be the calibration reference for corresponding channels on the other sounders in the constellation, such as SAPHIR and MHS. SAPHIR is a water vapor sounder, from the Megha-Tropiques (MT) satellite; and the Microwave Humidity Sounders (MHS) are instruments on NOAA and METOP satellites, that have similar channels. In order to reach an accurate humidity measurement it is necessary to intercalibrate these channels.

This paper presents the inter-calibration results for Microwave Humidity Sounder (MHS) instruments on NOAA-18, NOAA-19 and Metop-A against SAPHIR The Microwave Humidity Sounders have been inter-satellite cross-calibrated; before, and these MHS radiometers serve as the radiometric calibration standards for other satellite sounder radiometers. Unfortunately, inter-satellite radiometric calibration between similar radiometers is not negligible. Here the application of a “Double-Difference (DD)” XCAL technique to sounders has been investigated. This method has been successfully used for conical scanning microwave imagers before.

II. INTER SATELLITE CALIBRATION The Central Florida Remote Sensing Laboratory (CFRSL)

has developed the Double Difference (DD) technique for

XCAL, which has been successfully applied for imager channel calibration [1]. This inter-satellite calibration approach is to compare two satellite radiometer observations on a channel by channel basis (frequency/polarization combination), utilizing homogeneous earth scenes, that are collocated spatially and temporally. In the simplest sense, if two radiometer channels of identical design were to make an observation over the earth at the exact time and space, the difference in their Tb’s should reflect the radiometric calibration bias between the satellites. Unfortunately, for radiometers of different design the situation is more complicated because the scene brightness temperature varies with the observing frequency and viewing geometry (primarily earth incidence angle or EIA); therefore, normalization between the sensors is required before estimating the radiometric bias between the satellites. For the DD method, this normalization utilizes microwave radiative transfer theory, to translate the measurement of one or the other to a common basis before comparison.

The CFRSL’s inter-calibration algorithm is illustrated in Fig. 1, for SAPHIR and MHS. At the first step the data for two sensors is gridded to 1° x 1° boxes over the globe. Then these datasets are collocated globally. In order to model the atmosphere for the radiative transfer model, the information about the environmental parameters is necessary. The environmental data used here is from the NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analyses [7].

Fig. 1: diagram for CFRSL inter-satellite calibration procedure

211978-1-4799-4644-0/14/$31.00 ©2014 IEEE Microrad 2014

Fig. 2: SAPHIR and MHS channels, corresponding channels have been connected to each other

This product is from the Global Data Assimilation System (GDAS), which is prepared operationally every six hours, on a 1° x 1° lat/lon grid. After collocating and finding the corresponding environmental data, the theoretical and observed brightness temperature are calculated, using CFRSL radiative transfer model. The comparison between different sensors is performed on similar channels. The corresponding channels for SAPHIR and MHS sensors are shown in Fig. 2.

III. CROSS TRACK SCANNER MATCH-UPS A cartoon of cross track scanning is shown in the Fig. 3.

MHS instruments are on board of Polar orbiter satellites and Megha-Tropiques is a Low Earth Orbit satellite. Cross track scanners matchups have different Earth Incidence Angles (EIA) and different slant paths through the atmosphere, so filters for removing the matchups with non-homogeneous atmospheric conditions are required. For ocean, collocations with maximum distance of 25 Km is sufficient, which increases the number of match-ups. For land, closer collocations are required, and maximum distance of 10 Km is accepted. In this paper all the analysis has been done for data over Ocean. Best matchups occur when sensors look through nearly the same atmosphere path, and it happens for intersections near the satellite sub-tracks, where satellites view the surface at EIA’s < 15° and the differences between EIA’s are within < 5°.

Fig. 3: IFOV Match-ups for LEO and Polar have different EIA’s and Azimuth

angles

IV. DOUBLE DIFFERENCE In the DD XCAL technique the first step is to calculate

observed “Single Differences (SD)”, between the observed radiances (Tb’s) and theoretical Tb’s, which are calculated using an ocean radiative transfer model. This procedure normalizes the Tb observations to remove the effects of frequency and earth incidence angle (EIA) differences between radiometers. The final step is to calculate the double differences of the two single differences, which is the radiometric bias between channels.

For previous XCAL inter-satellite comparisons with microwave imagers, observations were spatially and temporally collocated in 1° Lat/Lng boxes, and biases were performed on binned average Tb’s that were heavily filtered to provide homogeneous clear-sky ocean scenes. However for cross-track sounders, this procedure was modified to include both comparisons at individual IFOV match-ups and binned averages over 1° boxes. Further, since SAPHIR & MHS are cross-track scanners, the DD bias determination is examined for scan-position dependent biases.

The process of calculating of DD is presented in the following equations:

Step1: OSD Tb = (Tb_MHS-obs - Tb_SAPHIR-obs)

Step2: MSD Tb= (Tb_MHS-sim - Tb_SAPHIR-sim)

Step3: DD = OSDTb - MSD Tb

In step1, the difference between observed brightness temperatures (Tb) of two sensors has been measured and it is called Observed Single Difference (OSD); in the second step the difference between modeled or simulated Tbs is calculated, this difference is called Modeled Single Difference (MSD).The last step calculates the difference between values of OSD and MSD, which gives the Double Difference values in step 3.

V. TRIPLE DIFFERENCE Triple differences (TD) are needed to map the NOAA-18

and -19 to Metop-A via SAPHIR. Triple difference is the difference of DD’s. In other words, TD’s are estimated for NOAA-18 and NOAA-19, with respect to Metop-A via SAPHIR for channels H3, H4 and H5. The following equations show the steps for calculating TDs for Channel H3 for NOAA18:

Step1: M18_H3_TD_1 = S18_S1H3 - SM_S1H3 Step2: M18_H3_TD_2 = S18_S2H3 - SM_S2H3

Step3: TD_M18_H3 = 0.5*(M18_H3_TD_1 + M18_H3_TD_2)

In Step1, the difference between DD for channel 1 of

SAPHIR and Channel3 of NOAA-18, with corresponding DD between SAPHIR and Metop-A, has been calculated. In Step2 the difference between DD for channel 2 of SAPHIR and Channel3 of NOAA-18, with corresponding DD between SAPHIR and Metop-A, has been calculated; and in Step3 the average of results of Step1 and Step2 represents the TD. The process for calculation of TD for other channels, like H4 and H5, is similar.

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VI. RESULTS The DD for SAPHIR-Metop-A, SAPHIR-NOAA-18 and

SAPHIR-NOAA-19 are calculated. Results presented in Fig. 5. show the DD between different channels of SAPHIR-METOP-A versus incidence angle. The circles show the average values over 5 degree incidence angle windows. It is important to note that the DD varies smoothly with the changing in the incidence angle.

In Table 1 results are presented for two different methods: 1) Binned average and 2) IFOV match-ups. For different sensors, SAPHIR/Metop-A (S/M), SAPHIR/NOAA-18 (S/N18) and SAPHIR/NOAA-19 (S/N19) are sub-divided into three different cross-over scenarios. Namely: “Case A”, where SAPHIR EIAs are close to nadir; “Case B”, where only MHS EIAs are close to nadir; and “Case C”, where the absolute differences between their EIAs are matched within ± 5°. These cases have been illustrated on Figure 4, where SAPHIR orbit has been indicated with blue and MHS with Orange. The collocated area is shown with dashed green line, and selected data in each case is the area with solid yellow line.

Moreover, conservative filters are applied to limit comparisons to clear sky oceanic observations, by using a convection filter that removes samples with rain and heavy clouds. In addition to the EIA constraints mentioned above, the following filters are applied: 1) for 1° boxes the number of observations must be ≥ 10 and the time coincidence between satellites < ±1 hour; 2) for IFOV match-ups, temporal match-ups must be ≤ 30 min, and the spatial collocation ≤ 25 Km. As it is seen, the results for different cases can be up to 0.5 kelvins different.

Fig. 5. Over ocean double differences for SAPHIR and Metop-A, using binned average for the Case C

TABLE I. TABLE I. DD FOR SAPHIR-METOP-A, SAPHIR-NOAA-18 AND SAPHIR-NOAA-19 FOR CASE A, B, AND C.

Fig. 4. SAPHIR and MHS orbit crossovers. From left to right: Case A: SAPHIR EIA are between ±5 degrees, Case B: MHS EIA are between ±5 degrees, Case C: differences of EIA between both sensor is less or equal to 5 degrees.

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The results for Triple Difference (TD) are presented in Table 2, for two different methods: 1) Binned average and 2) IFOV match-ups. For different sensors, SAPHIR/Metop-A (S/M), SAPHIR/NOAA18 (S/N18) and SAPHIR/NOAA19 (S/N19) are sub-divided into three different cross-over scenarios: Case A, Case B and Case C. As it is shown the values for TDs for all the cases are less than 0.4 kelvin, that shows that MHS sensors on NOAA18 and NOAA19 are well calibrated with regard to Metop-A.

TABLE II. TABLE II. DD FOR SAPHIR-METOP-A, SAPHIR-NOAA-18 AND SAPHIR-NOAA-19 FOR CASE A, B, AND C.

VII. CONCLUSION In this paper the application of CFRSL’s DD technique was

investigated. It was shown that the comparison of individual match-up’s or binned average approaches yield to nearly identical results. Also it was seen that the biases between the different MHS sensors are negligibly. Moreover the earth incidence angle dependence is negligible. We are going to calibrate the microwave sounder channels in Global

Precipitation Mission with Global Precipitation Mission Microwave Imager (GMI) sounder channels with other sounder channels in the GPM constellation.

ACKNOWLEDGMENT This research at CFRSL was performed under a grant with

NASA Headquarter Earth Science Program

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[6] Andrea Santos-Garcia , Saswati Datta, Hamideh Ebrahimi and, Linwood Jones, “Radiometric Inter-calibration of Microwave Humidity Sounders: MHS and SAPHIR”, X-CAL meeting with Megha-Tropiques team, CNES, Toulouse, France, May 23-24, 2013.

[7] NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analyses: http://rda.ucar.edu/datasets/ds083.2

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