Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2018, Vol. 12 Issue (1) : 1-16     https://doi.org/10.1007/s11707-017-0671-y
RESEARCH ARTICLE |
Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data
H. CHEN1, X. ZOU2(), Z. QIN3
1. Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, CO 80309, USA
2. Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, Maryland, MD 20742, USA
3. Joint Center for Data Assimilation Research and Applications, Nanjing University of Information Science and Technology, Nanjing 210044, China
Download: PDF(7028 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Measurements of brightness temperatures from Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding instruments onboard NOAA Polar-orbiting Operational Environmental Satellites (POES) have been extensively used for studying atmospheric temperature trends over the past several decades. Inter-sensor biases, orbital drifts and diurnal variations of atmospheric and surface temperatures must be considered before using a merged long-term time series of AMSU-A measurements from NOAA-15, -18, -19 and MetOp-A. We study the impacts of the orbital drift and orbital differences of local equator crossing times (LECTs) on temperature trends derivable from AMSU-A using near-nadir observations from NOAA-15, NOAA-18, NOAA-19, and MetOp-A during 1998−2014 over the Amazon rainforest. The double difference method is firstly applied to estimation of inter-sensor biases between any two satellites during their overlapping time period. The inter-calibrated observations are then used to generate a monthly mean diurnal cycle of brightness temperature for each AMSU-A channel. A diurnal correction is finally applied each channel to obtain AMSU-A data valid at the same local time. Impacts of the inter-sensor bias correction and diurnal correction on the AMSU-A derived long-term atmospheric temperature trends are separately quantified and compared with those derived from original data. It is shown that the orbital drift and differences of LECT among different POESs induce a large uncertainty in AMSU-A derived long-term warming/cooling trends. After applying an inter-sensor bias correction and a diurnal correction, the warming trends at different local times, which are approximately the same, are smaller by half than the trends derived without applying these corrections.

Keywords AMSU-A      diurnal adjustment      decadal temperature trend     
Corresponding Authors: X. ZOU   
Just Accepted Date: 25 September 2017   Online First Date: 14 November 2017    Issue Date: 23 January 2018
 Cite this article:   
H. CHEN,X. ZOU,Z. QIN. Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data[J]. Front. Earth Sci., 2018, 12(1): 1-16.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-017-0671-y
http://journal.hep.com.cn/fesci/EN/Y2018/V12/I1/1
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
H. CHEN
X. ZOU
Z. QIN
Satellite Paired with NOAA-18Start dateEnd date
NOAA-152005-05-242012-07-01
NOAA-192009-08-012013-03-28
MetOp-A2007-05-242011-04-23
Tab.1  The overlapping AMSU-A time periods for paired satellite datasets
Fig.1  Time series of double difference results for (a) channel 1 and (b) channel 5 of AMSU-A onboard NOAA-15 (N15), NOAA-19 (N19), MetOp-A (MOA) paired with AMSU-A onboard NOAA-18 (N18).
Fig.2  Scatter plots of double differences (OB)sat(OB)NOAA18 for AMSU-A channels 1 (left panels) and 5 (right panels). The inter-sensor bias μDD,?sat and one-standard deviation are indicated by red dot and red box, respectively.
Channelμsat/Kσsat/K
10.2720.241
20.0850.162
3–0.0010.145
4–0.1210.047
5–0.0810.027
6N/AN/A
70.5450.037
80.2320.048
90.7360.044
100.4990.056
11N/AN/A
120.4080.128
130.2040.215
14N/AN/A
150.3030.257
Tab.2  The inter-sensor biases for AMSU-A channels 1- 15 (except for channels 6, 11, 14) between NOAA-15 and NOAA-18. The inter-sensor biases between sat and NOAA-18 are estimated by μsat=(OB)sat(OB)NOAA18. σsat is the standard deviation
Channelμsat/Kσsat/K
1–0.1720.175
2–0.2310.112
3–0.1050.121
4–0.0160.028
5–0.1620.017
60.2310.017
70.4370.041
8N/AN/A
90.4850.037
100.4630.039
110.5020.056
120.3810.095
130.5940.147
140.2180.173
150.3190.179
Tab.3  Same as Table 2 except for NOAA-19 and NOAA-18
Channelμsat/Kσsat/K
10.1650.194
20.0690.127
3–0.0410.122
40.0410.034
5–0.0340.024
60.1620.027
7N/AN/A
80.2050.031
90.4420.043
100.4850.050
110.4590.076
120.1230.144
13–0.6510.241
14–0.7860.329
150.3670.184
Tab.4  Same as Table 3 except for MetOp-A and NOAA-18
Fig.3  Monthly mean of observational local times of multiple AMSU-As near-nadir observations from ascending nodes (solid lines) and descending nodes (dashed lines) over Amazon rainforest during 1998–2014.
Fig.4  Brightness temperature observations from AMSU-A channel 1 on (a) the descending nodes of NOAA-19, NOAA-18, NOAA-15 and MetOp-A which passed over Amazon rainforest around 0141 LT, 0227 LT, 0443 LT and 0949 LT January 1, 2012. AMSU-A FOV center locations are shown in black crosses; (b) the ascending nodes of NOAA-19, NOAA-18, NOAA-15 and MetOp-A over the Amazon rainforest around 1257 LT, 0227 LT, 0443 LT and 0949 LT.
Fig.5  Brightness temperature observations at FOVs 15 and 16 of channels 1, 5, 8 and 10 from NOAA-19, NOAA-18, NOAA-15 and MetOp-A under the “no-rain” conditions over Amazon rainforest in January (open circles) from 1999 to 2013. The monthly mean of near-nadir observations from descending nodes (down triangle) and ascending nodes (up triangle) are used for estimating the Fourier coefficients of the diurnal variation of brightness temperatures (black curve).
Fig.6  Time series of brightness temperature observations of channel 1 from NOAA-15 at its ascending (red, nighttime, early morning) and descending (blue, daytime, later afternoon) nodes (a) without and (b) with a diurnal correction using 1200 LT as reference local time. The linear trend of brightness temperature observations is indicated by black line. Regression equations are added in the upper right corner, the unit of T is day.
Fig.7  Same as Fig. 6 except for channel 5.
Fig.8  Time series of monthly mean brightness temperature observations of NOAA-15 AMSU-A channel 1 (a) without and (b)–(d) with a diurnal correction using a reference local time at (b) 0600 LT, (c) 0900 LT or (d) 1200 LT. The linear trend of brightness temperature observations is indicated by a black line. The data counts are indicated in grey shading. Regression equations are added in the upper right corner, the unit of T is month.
Fig.9  Monthly data counts of NOAA-15 AMSU-A observations under the “no-rain” (grey) and “raining” (red) conditions over Amazon rainforest during the time period from October 1998 to June 2012.
Fig.10  Linear trends of brightness temperatures derived from monthly mean of NOAA-15 AMSU-A channel 1 before (black) and after applying diurnal correction with three different reference local times of 0600 LT (red), 0900 LT (blue), and 1200 LT (green).
Fig.11  Time series of channel 1 near-nadir observations from NOAA-19, NOAA-18, NOAA-15 and MetOp-A (a) without any inter-sensor bias correction nor diurnal correction and with (b) inter-sensor bias correction only (c) with both the inter-sensor bias correction and diurnal correction using a reference time at 1200 LT. The black solid line indicates the linear trend. Regression equations are added in the upper right corner, the unit of T is day.
Fig.12  Decadal linear trends of brightness temperature observations for all AMSU-A channels from (a) NOAA-15 only and (b) NOAA-15, -18, -19 and MetOp-A.
1 Aldrich J (1998). Doing least squares: perspectives from Gauss and Yule. Int Stat Rev, 66(1): 61–81
https://doi.org/10.1111/j.1751-5823.1998.tb00406.x
2 Andersson E, Hollingsworth A, Kelly G, Lonnberg P, Pailleux J, Zhang Z (1991). Global observing system experiments on operational statistical retrievals of satellite sounding data. Mon Weather Rev, 119(8): 1851–1864
https://doi.org/10.1175/1520-0493(1991)119<1851:GOSEOO>2.0.CO;2
3 Cao C, Weinreb M, Xu H (2004). Predicting simultaneous nadir overpasses among polar-orbiting meteorological satellites for the intersatellite calibration of radiometers. J Atmos Ocean Technol, 21(4): 537–542
https://doi.org/10.1175/1520-0426(2004)021<0537:PSNOAP>2.0.CO;2
4 Clough S A, Shephard M W, Mlawer E J, Delamere J S, Iacono M, Cady-Pereira K E, Boukabara S, Brown P D (2005). Atmospheric radiative transfer modeling: a summary of the AER codes. J Quant Spectrosc Radiat Transf, 91(2): 233–244
https://doi.org/10.1016/j.jqsrt.2004.05.058
5 Derber J C, Wu W S (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev, 126(8): 2287–2299
https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2
6 Eyre J R, Kelly G A, McNally A P, Andersson E, Persson A (1993). Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteorol Soc, 119(514): 1427–1463
https://doi.org/10.1002/qj.49711951411
7 Ferraro R R, Weng F, Grody N C, Zhao L (2000). Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys Res Lett, 27(17): 2669–2672
https://doi.org/10.1029/2000GL011665
8 Han Y, Weng F, Liu Q, van Delst P (2007). A fast radiative transfer model for SSMIS upper atmosphere sounding channels. Journal of Geophysical Research: Atmospheres, 112(D11): D11121
https://doi.org/10.1029/2006JD008208
9 Kroodsma R A, McKague D S, Ruf C S (2012). Inter-calibration of microwave radiometers using the vicarious cold calibration double difference method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5: 1006–1013
10 Mears C A, Schabel M C, Wentz F J, Santer B D, Govindasamy B (2002). Correcting the MSU middle tropospheric temperature for diurnal drifts. Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International, 3: 1839–1841
11 Mo T (1996). Prelaunch calibration of the advanced microwave sounding unit-A for NOAA-K. IEEE Trans Microw Theory Tech, 44(8): 1460–1469
https://doi.org/10.1109/22.536029
12 Mo T (2007). Diurnal variation of the AMSU-A brightness temperatures over the Amazon rainforest. IEEE Transactions on Geoscience and Remote Sensing, 45: 958–969
13 Privette J L, Fowler C, Wick G A, Baldwin D, Emery W J (1995). Effects of orbital drift on advanced very high resolution radiometer products: normalized difference vegetation index and sea surface temperature. Remote Sens Environ, 53(3): 164–171
https://doi.org/10.1016/0034-4257(95)00083-D
14 Tian X, Zou X (2016). ATMS- and AMSU-A-derived hurricane warm core structures using a modified retrieval algorithm. J Geophys Res Atmos, 121(21): 12,630–12,646
https://doi.org/10.1002/2016JD025042
15 Wang L, Goldberg M, Wu X, Cao C, Iacovazzi R A, Yu F, Li Y (2011). Consistency assessment of atmospheric infrared sounder and infrared atmospheric sounding interferometer radiances: double differences versus simultaneous nadir overpasses. Journal of Geophysical Research: Atmospheres, 116( D11): 755–764
16 Weng F (2007). Advances in radiative transfer modeling in support of satellite data assimilation. J Atmos Sci, 64(11): 3799–3807
https://doi.org/10.1175/2007JAS2112.1
17 Weng F, Grody N C (2000). Retrieval of ice cloud parameters using a microwave imaging radiometer. J Atmos Sci, 57(8): 1069–1081
https://doi.org/10.1175/1520-0469(2000)057<1069:ROICPU>2.0.CO;2
18 Weng F, Zhao L, Ferraro R R, Poe G, Li X, Grody N C (2003). Advanced microwave sounding unit cloud and precipitation algorithms. Radio Sci, 38(4): 8068
https://doi.org/10.1029/2002RS002679
19 Zou C, Goldberg M D, Cheng Z, Grody N C, Sullivan J T, Cao C, Tarpley D (2006). Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses. Journal of Geophysical Research: Atmospheres, 111( D19): 5455–5464
20 Zou C, Wang W (2011). Intersatellite calibration of AMSU-A observations for weather and climate applications.  Journal of Geophysical Research: Atmospheres,  116( D23): 23113
21 Zou X, Wang X, Weng F, Li G (2011). Assessments of Chinese Fengyun Microwave Temperature Sounder (MWTS) measurements for weather and climate applications. J Atmos Ocean Technol, 28(10): 1206–1227
https://doi.org/10.1175/JTECH-D-11-00023.1
22 Zou X, Weng F, Yang H (2014). Connecting the time series of microwave sounding observations from AMSU to ATMS for long-term monitoring of climate. Journal of Atmospheric & Oceanic Technology, 31(10): 2206–2222
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed