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2206 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 31

Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

XIAOLEI ZOU Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida

FUZHONG WENG NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland

H. YANG Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

(Manuscript received 18 October 2013, in ﬁnal form 8 May 2014)

ABSTRACT

The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar- orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A ﬁelds of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

1. Introduction were launched successively through 2009. AMSU-A contains 15 channels. Channels 3, 5, 7, and 9 have simi- Since the ﬁrst launch of Television and Infrared lar frequencies to the four MSU channels. Today, the Observation Satellite (TIROS-N) in 1978, microwave Advanced Technology Microwave Sounder (ATMS) temperature sounding data have been used for both is ﬂying on board the Suomi National Polar-orbiting weather and climate applications. From 1978 to 2004, Partnership (Suomi-NPP) satellite and will be also on the National Oceanic and Atmospheric Administration board future U.S. Joint Polar Satellite System (JPSS) (NOAA) launched eight satellites (NOAA-6–NOAA-12 satellites, which are scheduled for launch in both 2017 and NOAA-14) carrying Microwave Sounding Unit and 2022. ATMS contains 22 channels. Channels 1–3 (MSU) instruments. MSU had four channels for sounding and 5–16 have the same central frequencies as the 15 atmospheric temperatures. Beginning in 1998, the MSU AMSU-A channels. instruments were replaced by the Advanced Microwave Polar-orbiting satellites circle the earth in sun- Sounding Unit-A (AMSU-A) when NOAA-15–NOAA-19 synchronous orbits with different equator crossing times (ECTs). NOAA-6, -8, -10, -12,and-15 are the morning satellites with their ECT occurring at either 0530 or Corresponding author address: Dr. X. Zou, Department of Earth, Ocean and Atmospheric Science, Florida State University, 0730 local time (LT). The satellites with ECT after 0930 LT Mail Code 4520, P.O. Box 3064520, Tallahassee, FL 32306-4520. are referring to midmorning satellites, such as NOAA-17, E-mail: [email protected] Meteorological Operation-A (MetOp-A) and MetOp-B,

DOI: 10.1175/JTECH-D-13-00232.1

Ó 2014 American Meteorological Society Unauthenticated | Downloaded 10/04/21 02:53 AM UTC OCTOBER 2014 Z O U E T A L . 2207 whereas those after 1300 LT are afternoon satellites, condition. As such, MSU, AMSU-A, and ATMS will such as TIROS-N, NOAA-7, -9, -11, -14, -16, -18, -19, provide a continuous time series for more than 35 years, and Suomi NPP. On all of these platforms, MSU, containing a wealth of information on global atmospheric AMSU-A and ATMS provide the measurements of the temperature. Although the MSU, AMSU-A, and ATMS global atmospheric temperature from the surface to the instruments are primarily designed for providing atmo- low stratosphere (MSU) and to the upper stratosphere spheric temperatures on a daily basis for predicting at- at about 1 hPa (AMSU-A and ATMS) and can also be mospheric weather systems, they are also applicable of used for climate trend studies. monitoring decadal climate changes of the atmospheric Soon after the launch of Suomi NPP, an intensive temperature because of the same channel frequencies calibration of the ATMS was carried out. The ATMS from different sensors, the sensor stability, and high cal- sensor data records have now reached a validated ma- ibration accuracy. turity level. For the JPSS program, both sensor data To derive atmospheric temperature trends from the record (SDR) and environmental data record (EDR) above-mentioned long-term MSU and AMSU-A data products are evaluated at three maturity levels. The beta series, satellite-to-satellite cross calibration must be car- maturity level is made when the initial calibration is ried out (Grody et al. 2004; Christy et al. 2000; Zou et al. applied and the product is minimally validated, and may 2006). Using a well-calibrated MSU–AMSU-A data se- still contain significant errors (Weng and Zou 2013). At ries, global warming ranging from 0.08 to 0.22 K per de- this level, the product is not appropriate for quantitative cade is detected in the lower troposphere represented by scientific publication, studies, or applications. The pro- MSU channel 2 (53.74 GHz) (Christy et al. 1998, 2000, visional maturity level is achieved when the instruments 2003; Mears et al. 2003; Mears and Wentz 2009; Vinnikov are calibrated and the products are validated but in- and Grody 2003;andZou et al. 2009). In this study, the cremental improvements are ongoing. The user com- ATMS data are first resampled into AMSU-like data and munity is encouraged to participate in the quality an intersensor calibration is then carried out for merging assurance and product validation processes. The vali- ATMS data into the AMSU-A data family. dated maturity level is reached when the on-orbit sensor The Backus–Gilbert (B-G) inversion method (Kirsch performance is characterized and calibration parameters et al. 1988; Caccin et al. 1992) is employed for the ATMS are adjusted accordingly. The data are ready for use by resampling purpose, which will be referred to as ATMS the operational center and scientific publications. Over- resample data. The B-G method solves an inverse prob- all, ATMS in-orbit performance is well characterized and lem that involves the integral equations. It can be viewed its noise equivalent differential temperature (NEDT) as an optimal linear interpolation filter in which the spatial meets the specification (Weng et al. 2013). The other resolution of the AMSU-A radiometer (i.e., the 3.38 ATMS features, such as channel specifications, bias beamwidth) and the antenna gain function of the ATMS characteristics, and impact of observation resolution on radiometer are preserved, the sensor noise can be con- capturing tropical storm structures, were described in trolled, and the data-processing artifacts are minimized. Weng et al. (2012, 2013). The scan biases related to Stogryn (1978) used the B-G method for estimating ATMS sidelobes and possible antenna emission were brightness temperatures from antenna temperatures mea- quantified by Weng et al. (2013) using the ATMS pitch- sured by a conical-scanning satellite-borne radiometer over maneuver data. A postlaunch calibration of ATMS system. Since the work by Stogryn (1978), the B-G algo- middle- and upper- tropospheric and stratospheric rithm was successfully applied to the Special Sensor Mi- channels (i.e., channels 5–15) was carried out by Zou et al. crowave Imager (SSM/I) satellite data (Poe 1990) to realize (2014) using the global positioning system (GPS) radio a spatial resolution enhancement of brightness tempera- occultation (RO) data. Preliminary assessments of the ture measurements from SSM/I (Poe 1990; Robinson et al. impact of ATMS data assimilation on hurricane track and 1992; Farrar and Smith 1992; Sethmann et al. 1994; Long intensity forecasts were reported in Zou et al. (2013). and Daum 1998). The lifetime of a given MSU or AMSU-A is limited While they all fly in sun-synchronous orbits, Suomi from a few years to more than 10 years. Fortunately, each NPP, NOAA-18, MetOp-A, and NOAA-15 satellites of these satellites overlaps other satellites. For example, have different ECTs. To collocate the data from any two during the period of 1998–2006, the last MSU on board different satellites during their overlapping period, Cao NOAA-14 operated as a backup, while the AMSU-A on et al. (2004) proposed a simultaneous nadir overpass the NOAA-15, -16,and-17 satellites became primary (SNO) method for intersatellite cross calibration. Yan ones. The Suomi NPP ATMS has been operational since and Weng (2008) used the SNO method for intersensor 2011, while AMSU-A on NOAA-15, -18,and-19 and calibration between the Special Sensor Microwave MetOp-A and MetOp-B are still operating in a healthy Imager/Sounder (SSM/IS) and SSM/I. In this study, the

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FIG. 1. ATMS FOVs (circles) used for remapping into an AMSU-like FOV (light gray) for ATMS channels (a) 3–16 and (b) 1 and 2 FOVs. Magnitudes of B-G coefﬁcients are indicated with colors. Integration time and satellite movement were taken into consideration.

SNO method is used for the intersensor calibration NOAA-18, MetOp-A,andNOAA-15. Section 2 pro- between AMSU-A and ATMS. Since the SNO matched vides a brief description of ATMS observations. The points from AMSU-A and ATMS only occur near the B-G method for optimally resampling the ATMS data to poles and the sample sizes since October 2011 are limited, AMSU-A-like observations is brieﬂy described in section the intersatellite calibration between AMSU-A and ATMS 3. Differences resulting from the resampling within is still preliminary and will be further reﬁned as more Hurricane Sandy are illustrated in section 4. Section 5 SNO data pairs are collected in the future. presents numerical results of intersensor calibration This work describes in detail a set of intersensor between ATMS on board Suomi NPP and AMSU-A calibration between ATMS and AMSU-A on board on board NOAA-15, NOAA-18, and MetOp-A using

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FIG. 2. (a) ATMS LWP from Suomi-NPP and (b) MHS IWP retrievals and (c) brightness temperature observations of AVHRR channel 4 (10.8 mm) from NOAA-18 within Hurricane Sandy at 0600 UTC 28 Oct 2012. Hurricane Sandy is located at 31.38N, 73.78W. SLP from NCEP GFS fields is plotted using contours at 5-hPa intervals. the SNO method. Section 6 provides a summary and difference in the size of the field of view (FOV) be- conclusions. tween ATMS and AMSU (Weng et al. 2013). Another major difference between ATMS and AMSU-A arises from the scanning mode. Since AMSU-A scans the 2. Differences of channel characteristics between earth in a step-and-stare mode, there is no overlapping ATMS and AMSU-A between neighboring FOVs in both cross-track and ATMS is a total power cross-track radiometer, and it along-track directions. However, ATMS FOVs are scans the earth within the range of 652.778 with respect overlapping because of its continuously scanning to the nadir direction. It has a total of 22 channels. The mode, which results in significant oversampling in both first 16 channels are primarily for temperature soundings cross-track and along-track directions. A single FOV from the surface to about 1 hPa (;45 km) and are in- of either ATMS channel 1 or 2 overlaps partially with vestigated in this study. The remaining channels, 17–22, its neighboring four FOVs and four scan lines. A single are for humidity soundings in the troposphere from the FOV of any of the ATMS channels 3–16 overlaps with surface to about 200 hPa (;12 km) and are not included itsthreeneighboringFOVsandthreenearbyscan in this study. lines. The antenna beamwidth of ATMS is different from that of AMSU-A. All AMSU-A channels have a beam- 3. Generation of AMSU-A-like ATMS resample width of 3.38. However, the ATMS channels 3–16 have data a smaller beamwidth of 2.28 and that of ATMS channels 1 and 2 is 5.28. The differences in the beamwidth be- To link ATMS observations with AMSU-A observa- tween ATMS and AMSU channels result in a significant tions from all prior NOAA polar-orbiting satellites,

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FIG. 3. ATMS brightness temperature observations for (a),(b) channel 1, (c),(d) channels 16 and 5 (left) before and (right) after resampling within Hurricane Sandy at 0600 UTC 28 Oct 2012. SLP from NCEP GFS ﬁelds is plotted using contours (interval: 5 hPa). Differences introduced by resampling for channels 1 and 16 are shown by contours at (left) 1-K intervals for channels 1 and 16 and (right) 0.4-K intervals for channel 5.

ATMS brightness temperatures are ﬁrst resampled to time for each ATMS FOV and Suomi NPP satellite the AMSU-A-like observations. Differences in FOVs movement is taken into consideration in all the FOVs between ATMS and AMSU-A are illustrated in Fig. 1. shown in Fig. 1. Thus, ATMS data from neighboring Speciﬁcally, Fig. 1a shows an AMSU-A FOV and its points at channels 3–16 can be convolved, whereas nearby 5 3 5 FOVs for ATMS channels 3–16, whereas those at channels 1 and 2 must be deconvolved to Fig. 1b plots an AMSU-A FOV and its nearby 3 3 3 AMSU-like FOVs. Note that in the current Suomi FOVs for ATMS channels 1 and 2. The integration NPP ground processing system, the ATMS resample

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The following is a brief description of the B-G algorithm based on the work by Stogryn (1978). Specifically, the B-G algorithm is a process of finding a set of optimal weighting coefficients (w) so that the kth AMSU-like BG ATMS data, Tb , can be expressed as a linear combination of adjacent ATMS brightness temperatures:

Nch BG 5 1 ATMS 1 Tb (k) å w(k i, j) Tb (k i, j), (1) 52 i,j Nch

where Nch is a resample window size parameter and k 5 1, ..., 96. Assuming that the portion of the earth viewed by the AMSU-A antenna at the kth scan angle from the sat- FIG. 4. Standard deviations (s) of brightness temperatures for ellite altitude is Ak, the brightness temperatures over the ATMS channels 1–9 and 16 within Hurricane Sandy before (bar region of Ak observed by ATMS can also be expressed as with slashes) and after (solid bar) resampling using the same do- ð main as in Figs. 3–5. Positive (pink) and negative (cyan) differences ATMS 1 5 BG of standard deviations before (sATMS) and after (sATMS/AMSU) Tb (k i, j) GkTb (k) dA, (2) resample (Ds 5 sAMSU)ATMS 2 sATMS) are indicated by the y axis Ak1i,j on the right. where G is the AMSU-A antenna gain function and dA sensor data record (RSDR) at channels 3–16 is available, is the elementary surface area on the earth. The con- but the convolution is done with respect to the Cross- tributions from the cosmic background radiation and Track Infrared Sounder (CrIS) of regard (FOR), which is possible contributions from the spacecraft itself are very similar to the AMSU-A FOV. The FOV at ATMS chan- small and neglected in Eq. (2). nels 1 and 2 in the RSDR remains the same as their By multiplying w(k 1 i, j) on both sides of Eq. (2) and original FOV, which corresponds to the beamwidth of 5.28. taking the summation over (k 1 i, j), we can obtain

ð " # Nch Nch å 1 ATMS 1 5 å 1 BG w(k i, j) Tb (k i, j) w(k i, j)Gk Tb (k) dA. (3) 52 52 i,j Nch Ak i,j Nch

If the bracketed term in Eq. (3) were a delta function then Eq. (3) would turn into Eq. (1). Thus, if the weighting r(ki,j 2 k), that is, coefﬁcients could be derived so that the left-hand side of Eq. (4) approaches a delta function, Eq. (1) could serve an ap- Nch å 1 5 2 proximation of the exact solution when Eq. (4) is held true. w(k i, j)Gk r(ki,j k), (4) 52 The B-G resampling algorithm searches for the opti- i,j Nch mal weighting coefﬁcients by minimizing the following least squares problem:

ð " #2 Nch 1 5 å 1 2 1 Q0[w(k i, j)] w(k i, j)Gk F(k i, j) dA (5) 52 Ak i,j Nch under the following constraint: region. The constraint [Eq. (6)] simply ensures a proper ð normalization of the results. Thus, if a scene with a uni- Nch å 1 5 form brightness temperature were viewed, then the w(k i, j)Gk dA 1, (6) 52 Ak i,j Nch brightness temperature derived from Eqs. (1) and (3) where the function F is chosen to be a constant 1/A0 over would be exactly the brightness temperature of the some speciﬁed region A0 (see Fig. 2) and 0 outside this scene observed by an AMSU-A antenna.

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FIG. 5. Scatterplots of brightness temperature difference between resampled ATMS and AMSU-A data as a function of the resampled ATMS brightness temperature for ATMS channels 6–11. Outliers that are removed from cross calibration are indicated in red. ATMS channel numbers are indicated in each panel.

While obtaining an estimate at the desired AMSU-A Instead of minimizing, Q0 is deﬁned in Eq. (5), the fol- resolution, one would also like to minimize the noise lowing quantity is minimized: variance for the estimate, TBG. Assuming OATMS is the b 5 g 1 s2 b g error covariance matrix of the ATMS measurements, Q(w) Q0(w) cos BG sin , (8) the noise variance for the estimate can be expressed as where g is a parameter whose value varied from 0 to p/2 2 5 TOATMS sBG w w. (7) and b is simply a convenient scale factor that is chosen to

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TABLE 1. Mean differences of brightness temperatures (K) from TABLE 2. Mean, as well as the intercept and slope of linear re- SNO data of Suomi ATMS (resample) and AMSU-A during the gression of the differences of brightness temperature (K) between time period from 1 Jan 2012 to 31 Mar 2013. MetOp-A AMSU-A and NOAA-18 AMSU-A derived from SNO data found during the time period from 1 May 2007 to 31 Dec 2012. ATMS resample minus AMSU-A Channel Mean Intercept Slope MetOp-A minus NOAA-18 Channel Mean Intercept Slope 1 20.25 20.22 20.0002 2 0.08 20.20 0.0015 1 0.04 0.23 20.0009 3 20.35 20.01 20.0016 2 20.04 0.51 20.0027 5 0.15 20.74 0.0039 3 0.25 1.00 20.0034 6 20.29 24.66 0.0189 4 0.16 1.29 20.0048 7 0.99 2.73 20.0077 5 0.16 0.83 20.0029 8 0.70 5.12 20.0199 6 20.03 0.36 20.0017 9 20.30 1.31 20.0074 7 20.12 1.80 20.0087 10 0.58 2.29 20.0079 8 20.14 20.13 20.0001 11 0.59 3.66 20.0141 9 20.26 0.06 20.0015 12 0.60 3.06 20.0112 10 20.35 20.04 20.0014 13 0.26 2.35 20.0092 11 20.41 20.63 0.0010 14 0.18 1.61 20.0061 12 20.35 0.08 20.0019 15 0.08 1.82 20.0070 13 20.18 21.15 0.0041 16 20.05 1.95 20.0102 14 0.03 0.27 20.0010 15 0.01 0.07 20.0003

2 make Q0 and sBG dimensionally the same but is other- wise arbitrary. When g is increased from 0 to p/2, less B-G coefficients, w(k 1 i, j), where i represents the three emphasis is placed on resolution and more emphasis is nearby FOVs and j represents the three nearby scan lines. BG placed on noise reduction in the estimate of Tb , which The B-G coefficients for the two FOVs having the same is the AMSU-like ATMS resample data. scan angle with respect to the nadir are the same, The solution of the minimization problem [Eq. (8)] namely, the B-G coefficients for the second ATMS FOV under the constraint [Eq. (6)] was provided in Stogryn are the same as those for the 95th ATMS FOV, the third (1978) and would give the best estimate in the minimum ATMS FOV are the same as those for the 94th ATMS variance sense. The estimate represents the average bright- FOV, etc. ness temperature within a specified region (i.e., the The B-G coefficients for the conversion of the ATMS AMSU-A FOV) based on the available measurements antenna temperatures to an AMSU-A-like FOV at nadir of ATMS antenna temperatures. for channels 3–16 and channels 1 and 2 are also shown in 5 : The b value is taken to be b 0 001, which makes the Fig. 1. It is seen that the largest weighting coefficient is two terms in Eq. (8) of similar magnitudes. Three different window sizes (e.g., 3, 5, and 7) are assigned to the window size parameter Nch. The value of g is determined TABLE 3. Mean, intercept, and slope of linear regression of the by requiring that the constraint [Eq. (6)] is best satisfied, differences of brightness temperature (K) between NOAA-15 which results in g 5 0:1 for downscaling of ATMS chan- AMSU-A and NOAA-18 AMSU-A derived from SNO data found nels 3–16, and g 5 0:1 3 1023 for resolution enhancement during the time period from 1 Jan 2006 to 31 Dec 2011. of ATMS channels 1 and 2, when producing the ATMS NOAA-15 minus NOAA-18 resample ATMS data. It is also found that the best win- Channel Mean Intercept Slope dow size is three for ATMS channels 1 and 2 and five for 2 2 ATMS channels 3–16. 1 0.05 1.78 0.0085 2 0.04 21.83 0.0093 There are 94 AMSU-A-like FOVs whose geographic 3 0.05 20.14 0.0009 locations are the same as ATMS FOVs 2–95. For each of 4 20.25 21.66 0.0060 the 94 AMSU-A-like FOVs into which the ATMS chan- 5 20.07 0.29 20.0015 nels1and2dataaremapped,therearenineB-Gco- 6 0.04 1.21 20.0052 2 efficients, w(k 1 i, j), where i represents the three nearby 7 0.54 3.17 0.0119 8 0.23 0.02 0.0010 FOVs and j represents the three nearby scan lines. For 9 0.51 1.92 20.0066 ATMS channels 3–16, there are 92 AMSU-A-like FOVs 10 0.49 2.12 20.0079 whose geographic locations are the same as ATMS FOVs 12 0.30 2.13 20.0081 3–94. For each of the 92 AMSU-A-like FOV into which 13 0.38 3.12 20.0116 2 the ATMS channels 3–16 data are mapped, there are nine 15 0.27 3.35 0.0150

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22 FIG. 6. Brightness temperatures at nadir within 28S–28N, 808–1008W under clear sky (e.g., LWP , 0.01 kg m ) (top) before and (bottom) after SNO corrections for AMSU-A channel 6 from NOAA-15 (black), NOAA-18 (green), MetOp-A (red), and ATMS channel 7 (blue). given to the ATMS FOV in the center, which coincides in the Caribbean Sea on 22 October and a subsequent with the ATMS resample. The farther away the FOVs are northward movement over the Bahamas before 28 Oc- from the ATMS resample, the smaller the B-G weights tober. Hurricane Sandy was the largest Atlantic hurri- are. The overlapping characteristics of ATMS ensure that cane on record, and made landfall near Atlantic City, such a weighted average approximates a realistic map- New Jersey, on 30 October 2012. ping of ATMS data onto the AMSU-A type of data. Figure 2 shows the liquid water path (LWP) derived from ATMS channels 1 and 2 (Fig. 2a), the ice water path (IWP) derived from Microwave Humidity Sounder 4. Differences introduced by ATMS resampling (MHS) channels 1 and 2, as well as brightness temper- within Hurricane Sandy ature observations of Advanced Very High Resolution To examine the differences of brightness tempera- Radiometer (AVHRR) infrared channel 4 (10.8 mm) tures introduced by the resampling of ATMS data, we within Hurricane Sandy at 0600 UTC 28 October 2012. choose a hurricane case that often occupies several Both MHS and AVHRR are on board NOAA-18. The thousand kilometers with complicated small-scale fea- sea level pressure (SLP) from the National Centers for tures. Speciﬁcally, ATMS observations and their resam- Environmental Prediction (NCEP) Global Forecast Sys- ple data are compared within Hurricane Sandy on 28 tem (GFS) ﬁelds is also plotted using contours at 5-hPa October 2012. This is the time when Sandy started to intervals. There were two rainbands north and west of move northwestward from its initial westward movement the center of Hurricane Sandy, which was located at

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FIG.7.AsFig. 6, but for AMSU-A channel 7 and ATMS channel 8.

31.38N, 73.78W at 0600 UTC 28 October 2012. The and 16 are overlapped with the resampled brightness 2 values of both LWP and IWP exceeded 1.5 kg m 2. temperatures in Figs. 3b,d (e.g., contours at 1-K in- Because of higher resolutions of MHS (;15 km) and tervals). Both channels 1 and 16 are located at the win- AVHRR (;4 km) than ATMS channels 1 and 2 dow regions of atmospheric oxygen absorption spectra. (;75 km), Hurricane Sandy’s eye is better captured by The ATMS channel 1 has the lowest center frequency at the MHS-derived IWP and AVHRR infrared channel 4 23.8 GHz among the 16 ATMS temperature sounding than the ATMS low-frequency-channel-derived LWP. channels. Microwave measurements at this window chan- There were more liquid clouds than ice clouds in terms nel are still quite sensitive to clouds and water vapor of spatial coverage. The southeast half of Hurricane emission. It is one of the two key channels used for physical Sandy was mostly under clear-sky conditions at this retrievals of LWP and water vapor path (WVP) through time. This allows the differences of brightness temper- an emission-based radiative transfer model (Greenwald atures introduced by the resampling of ATMS data to be et al. 1993; Weng and Grody 1994; Weng et al. 1997; Weng examined in both cloudy and clear-sky conditions. and Grody 2000; Wentz 1997; Weng et al. 2003). By The ATMS brightness temperature observations for comparing ATMS brightness temperature observations channels 1 and 16 before (Figs. 3a,c)andafter(Figs. 3b,d) (Fig. 3a)withLWP(Fig. 2a), it is found that the measured the resampling within Hurricane Sandy at 0600 UTC brightness temperatures at 23.8 GHz are higher over hur- 28 October 2012 are presented in Fig. 3. For clarity, the ricane rainbands, reﬂecting the contributions from cloud differences introduced by the resampling for channels 1 and water vapor emission.

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FIG.8.AsFig. 7, but for data within 28S–28N, 1508–1708E.

The center frequency of channel 16 is 88.2 GHz, which (2.28 beamwidth) is resampled to AMSU-A resolution is the highest among the 16 ATMS temperature (3.38 beamwidth), the data resolution is reduced. Results sounding channels. Microwave measurements at this in Fig. 3d conﬁrm that the brightness temperatures after frequency are affected by both the emission and scat- resampling become smoother. The maximum brightness tering of clouds. Over areas with ice clouds (Fig. 2b), the temperatures over areas with liquid clouds become ATMS-measured brightness temperatures at 88.2 GHz lower, and the minimum brightness temperatures over (Fig. 3c) could be more than 25 K colder than those over ice cloud areas become larger after resampling. As ex- their surrounding areas with liquid water clouds. The pected, positive and negative differences resulting from resampling of ATMS channel 1 data (Fig. 3a), which has the resample are found over the minimum and maxi- a 5.28 beamwidth, to AMSU-A-like resolution with mum brightness temperatures areas, respectively. a 3.38 beamwidth (Fig. 3b) does enhance the observation Impacts of the resampling on ATMS temperature resolutions. After resampling, the maximum brightness sounding channels are qualitatively similar to ATMS temperatures over cloudy areas become even larger, the channel 16, but the differences introduced by the resam- minimum brightness temperatures over clear-sky streaks pling in brightness temperatures are of much smaller among clouds become even smaller, and the gradients magnitudes, as shown in Figs. 3e,f for ATMS channel 5. of brightness temperatures near cloud edges become Impacts on other channels with higher peak weighting sharper. On the contrary, when ATMS channel 16 data function altitudes become even smaller (ﬁgures omitted).

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FIG.9.AsFig. 8, but for AMSU-A channel 7 and ATMS channel 8.

The standard deviations of brightness temperatures resampling decrease rapidly with the increase of channel for ATMS channels 1–9 and 16 within the domain shown number for ATMS temperature sounding channels. in Fig. 3 for Hurricane Sandy before (sATMS) and after (s ) the resampling are provided in Fig. 4. The dif- BG 5. Cross calibration between ATMS resample and ferences of standard deviations between s and ATMS AMSU-A data sBG—that is Ds 5 sBG 2 sATMS—are also indicated in Fig. 4. It is seen that a resolution enhancement by the The SNO points between Suomi NPP ATMS and resampling corresponds to an increase of the standard NOAA-18 AMSU-A are identiﬁed during a 16-month deviations of ATMS channels 1 and 2 on the order of period from 1 January 2012 to 31 March 2013 and are 0.4–0.8 K. A resolution reduction by the resampling for used for characterizing the relative biases between two ATMS channels 3–16 corresponds to a decrease of the instruments. In addition, AMSU-A data from NOAA-15, standard deviations of measured brightness tempera- NOAA-18, and MetOp-A satellites are also collocated at tures within Hurricane Sandy. It is therefore concluded SNO locations. The SNO points are found under the that the B-G resampling algorithm acts as a smoother criteria of a spatial distance and temporal separation for ATMS channels 3–16, and is capable of producing being less than 15 km and 60 s, respectively. These SNO higher-resolution data from overlapping coarser- points of Suomi NPP and NOAA-18 satellites are then resolution ATMS data for ATMS channels 1 and 2. used for deriving a linear regression function of the bias Differences in standard deviations introduced by the between the two data types (i.e., ATMS resample and

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FIG. 10. Time series of the ocean mean clear-sky brightness temperature bias between two satellites for AMSU-A channel 5–13 from the pentad dataset within the latitudinal zone of 308S–308N. Black and red lines present the bias at AMSU-A channels among the NOAA-15, NOAA-18, and METOP satellites, whereas the blue line stands for the bias at ATMS channels 6–14 and AMSU-A channels 5–13 between the Suomi-NPP and NOAA-18 satellites. (left) Results from the original data and (right) results after the cross calibration.

AMSU-A) as a function of the ATMS brightness tem- AMSU-A as a function of the ATMS resampled bright- perature for each pair of ATMS–AMSU-A channels. ness temperature. Here, the AMSU-A channel number Figure 5 shows the SNO brightness temperature dif- (e.g., channel 5) is compared with the corresponding ferences between Suomi NPP ATMS and NOAA-18 ATMS channel number (e.g., channel 6). Outliers with

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FIG. 10. (Continued)

SNO differences between the two instruments (e.g., the bias between two instruments is slightly scene de- ATMS and AMSU-A) being more than one standard pendent, which is probably due to the different non- deviation are removed. Impact of rain-induced saturation linearity correction in the radiometric calibration. on the resampling is thus mostly eliminated. The differ- The SNO data points after quality control are used for ences between ATMS resample and AMSU-A data after deriving an intersatellite bias offset through a linear the quality control step agree in general within 61K,and regression for each channel:

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5 1 offset between ATMS resample data and AMSU-A mch achTb,ch bch , (9) data. After the SNO calibration, the intersatellite where the subscript ch indicates the ATMS channel biases between ATMS resample data and AMSU-A number. Values of the slope ach and the intercept bch of data from NOAA-15 and MetOp-A are significantly the regression [Eq. (9)] for all the paired channels be- reduced for all eight paired ATMS–AMSU-A chan- tween ATMS and AMSU-A from NOAA-18 are pro- nels. Similar results are obtained in the middle and high vided in Table 1. latitudes (figures omitted). It is reminded that MetOp-A A similar procedure for SNO cross calibration is also AMSU channel 7 has been degrading in performance carried out between MetOp-A and NOAA-18, and be- since 16 December 2008 (http://www.oso.noaa.gov/ tween NOAA-18 and NOAA-15. The SNO points be- poesstatus/componentStatusSummary.asp?spacecraft5 tween MetOp-A and NOAA-18 are found during the 2&subsystem51) with noise exceeding specifications. time period from 1 May 2007 to 31 December 2012, and This degradation in data quality of NOAA-15 AMSU-A those between NOAA-15 and NOAA-18 are found dur- channel 7 is clearly seen in the temporal evolution of the ing the time period from 1 January 2006 to 31 December intersatellite biases shown in Fig. 10. The slow deterioration 2011. The mean, as well as the intercept and slope of in measurement accuracy of NOAA-15 AMSU-A channel a linear regression of the differences of brightness tem- 6(Mo 2009) was also seen from the intersatellite biases perature between MetOp-A AMSU-A and NOAA-18 (Fig. 10). In addition, the effects of AMSU-A instrument AMSU-A derived from SNO points are provided in aging and NOAA-15 satellite orbital drift are reflected in Table 2. Similar results for NOAA-15 and NOAA-18 are the intersatellite biases between NOAA-15 and NOAA-18 provided in Table 3. and can also be detected in stratospheric channels. Changes in brightness temperatures through the SNO cross calibration described above are illustrated in Figs. 6–9 6. Summary and conclusions for various ATMS and AMSU channels. Figures 6 and 7 provide brightness temperature observations at nadir The AMSU-A instruments have been on board within the area (28S–28N, 808–1008W) over the tropical NOAA and European meteorological satellites since Atlantic Ocean under clear-sky conditions. The clear 1998. They were replaced by the Advanced Technology sky is defined by a LWP that is derived from AMSU-A Microwave Sounder (ATMS) on board Suomi-NPP as 2 channels 1 and 2, being less than 0.01 kg m 2 (Weng well as future Joint Polar Satellite System (JPSS) satellites. et al. 2003). Before SNO corrections, AMSU-A data While allowing for improved applications of micro- at channels 6 and 7 from NOAA-15, NOAA-18, and wave temperature sounding measurements to numerical MetOp-A and ATMS channels 7 and 8 from Suomi NPP weather prediction, some modifications in ATMS’s display some significant biases with respect to each channel frequency, data resolutions, beamwidth, and other. Some of the biases may be related to the diurnal overlapping FOVs present new challenges for merging effects of atmospheric temperature profiles. Figures 8 and ATMS data with data from its predecessor heritage in- 9 are similar to Figs. 6 and 7, except for a different area struments AMSU-A and MHS combined. over the tropical Pacific Ocean. It is seen that the ATMS This paper first applies the B-G method for converting data have no systematic biases and can be well merged the ATMS data to the ATMS resample data, which are with the AMSU-A data series after cross calibration. AMSU-A-like observations. This work is required for Figure 10 presents a time series of the brightness extending the time series of microwave temperature temperature bias at nadir between eight paired ATMS– sounding observations for the purpose of monitoring AMSU-A channels (e.g., AMSU-A channel numbers 5–10 climate. A subpixel microwave antenna temperature and 12 and 13) over oceans in clear-sky atmospheric simulation technique is employed for deriving a set of conditions within a latitudinal zone of 308S–308N before optimal weighting coefficients. An SNO matchup data- and after SNO calibration. AMSU-A on board NOAA-18 set for nadir pixels with criteria of simultaneity of less was used as a reference. AMSU-A channels 11 and 14 than 60 s and within a ground distance of 15 km is then are not shown in Fig. 10 because NOAA-15 AMSU-A generated for all overlaps of the ATMS resample and channel 14 has not been working since October 2000, AMSU-A on board NOAA-18 from 1 January 2012 to and AMSU-A channel 11 failed in April 2002 (Mo 31 March 2013. The simultaneous and overlapping na- 2009). Because of the significant influences of orbital ture of these matchups eliminates the impact of orbital drift and surface emissivity on the accuracy of surface- drifts and reduces weather-related impacts on the cali- sensitive measurements, the measurements by AMSU-A bration. Finally, a combination of the B-G method with channels 1–4 and 15 are not considered in this study. It is the SNO allows for better merging of ATMS data with seen that before SNO calibration, there exists a significant AMSU-A data.

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