scholarly journals On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances

2019 ◽  
Vol 19 (6) ◽  
pp. 3733-3746
Author(s):  
Klaus Gierens ◽  
Kostas Eleftheratos
Keyword(s):  
High Resolution ◽  
Water Vapour ◽  
Perfect Match ◽  
Saturation Pressure ◽  
Calibration Method ◽  
Second Order ◽  
Brightness Temperatures ◽  
Average Brightness ◽  
Upper Tropospheric Humidity ◽  
Infrared Radiances

Abstract. We present a novel retrieval for upper-tropospheric humidity (UTH) from High-resolution Infrared Radiation Sounder (HIRS) channel 12 radiances that successfully bridges the wavelength change from 6.7 to 6.5 µm that occurred from HIRS/2 on National Oceanic and Atmospheric Administration satellite NOAA-14 to HIRS/3 on satellite NOAA-15. The jump in average brightness temperature (in the water vapour channel; T12) that this change had caused (about −7 K) could be fixed with a statistical inter-calibration method (Shi and Bates, 2011). Unfortunately, the retrieval of UTHi (upper-tropospheric humidity with respect to ice) based on the inter-calibrated data was not satisfying at the high tail of the distribution of UTHi. Attempts to construct a better inter-calibration in the low T12 range (equivalent to the high UTHi range) were either not successful (Gierens et al., 2018) or required additional statistically determined corrections to the measured brightness temperatures (Gierens and Eleftheratos, 2017). The new method presented here is based on the original one (Soden and Bretherton, 1993; Stephens et al., 1996; Jackson and Bates, 2001), but it extends linearisations in the formulation of water vapour saturation pressure and in the temperature dependence of the Planck function to second order. To achieve the second-order formulation we derive the retrieval from the beginning, and we find that the most influential ingredient is the use of different optical constants for the two involved channel wavelengths (6.7 and 6.5 µm). The result of adapting the optical constant is an almost perfect match between UTH data measured by HIRS/2 on NOAA-14 and HIRS/3 on NOAA-15 on 1004 common days of operation. The method is applied to both UTH and UTHi. For each case retrieval coefficients are derived. We present a number of test applications, e.g. on computed brightness temperatures based on high-resolution radiosonde profiles, on the brightness temperatures measured by the satellites on the mentioned 1004 common days of operation. Further, we present time series of the occurrence frequency of high UTHi cases, and we show the overall probability distribution of UTHi. The two latter applications expose indications of moistening of the upper troposphere over the last 35 years. Finally, we discuss the significance of UTH. We state that UTH algorithms cannot be judged for their correctness or incorrectness, since there is no true UTH. Instead, UTH algorithms should fulfill a number of usefulness postulates, which we suggest and discuss.

scholarly journals Determination and significance of upper-tropospheric humidity

2018 ◽  
Author(s):  
Klaus Gierens ◽  
Kostas Eleftheratos
Keyword(s):  
Optical Constants ◽  
Weighting Function ◽  
Perfect Match ◽  
Saturation Pressure ◽  
Second Order ◽  
Upper Troposphere ◽  
Brightness Temperatures ◽  
Average Brightness ◽  
Upper Tropospheric Humidity ◽  
Average Brightness Temperature

Abstract. We present a novel retrieval for upper-tropospheric humidity (UTH) from HIRS channel 12 radiances that successfully bridges the wavelength change from 6.7 to 6.5 µm that occurred from HIRS 2 on NOAA 14 to HIRS 3 on NOAA 15. The jump in average brightness temperature (T12) that this change caused (about −7 K) could be fixed with a statistical intercalibration method (Shi and Bates, 2011). Unfortunately, the retrieval of UTHi based on the intercalibrated data was not satisfying at the high tail of the distribution of UTHi. Attempts to construct a better intercalibration in the low T12 range (equivalent to the high UTHi range) were either not successful (Gierens et al., 2018) or required additional statistically determined correctionsto the measured brightness temperatures (Gierens and Eleftheratos, 2017). The new method presented here is based on the original one (Soden and Bretherton, 1993; Stephens et al., 1996; Jackson and Bates, 2001), but it extends linearisations in the formulation of water vapour saturation pressure and in the temperature-dependence of the Planck function to second order. To achieve the second-order formulation we derive the retrieval from the beginning, and we find that the most influential ingredient is the use of different optical constants for the two involved channel wavelengths (6.7 and 6.5 µm). The result of adapting the optical constant is an almost perfect match between UTH data measured by HIRS 2 on NOAA 14 and HIRS 3 on NOAA 15 on 1004 common days of operation. The method is applied to both UTH and UTHi, the upper-tropospheric humidity with respect to ice. For each case retrieval coefficients are derived. We present a number of test applications, e.g. on computed brightness temperatures based on high-resolution radiosonde profiles, on the brightness temperatures measured by the satellites on the mentioned 1004 common days of operation. Further we present time series of the occurrence frequency of high UTHi cases and we show the overall probability distribution of UTHi. The two latter applications expose clear indications of moistening of the upper troposphere over the last 35 years. Finally, we discuss the significance of UTH. We state that UTH algorithms cannot be judged for their correctness or incorrectness, since there is no true UTH. Instead, UTH algorithms should fulfil a number of usefulness-postulates, that we suggest and discuss. In the course of this discussion an alternative method to estimate the weighting function is presented.

scholarly journals Characteristics of VIRS Signals within Pixels of TRMM PR for Warm Rain in the Tropics and Subtropics

2017 ◽  
Vol 56 (3) ◽  
pp. 789-801 ◽  
Author(s):  
Yilun Chen ◽  
Yunfei Fu
Keyword(s):  
High Resolution ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
Low Resolution ◽  
Clear Sky ◽  
Data Merging ◽  
Brightness Temperatures ◽  
Average Brightness ◽  
Warm Rain ◽  
The Tropics

AbstractMany data-merging studies of the Tropical Rainfall Measuring Mission (TRMM) satellite involve the integration of high-resolution Visible and Infrared Scanner (VIRS) signals (~2 km) with low-resolution Precipitation Radar (PR) footprint (~5 km) to obtain comprehensive information from observations. Based on the merged dataset, “warm rain” is generally identified as having averaging 10.8-μm brightness temperatures (TB10.8) exceeding 273 K and the existence of surface rainfall. However, this integration may lead to the misidentification of warm rain because the beam-filling problem (nonuniform TB10.8 in PR pixels) is not fully considered through the method using high-resolution TB10.8 to match low-resolution rainfall. To assess the bias that is associated with identifying warm rain, a new dataset that includes all VIRS signals within the PR resolution is established, and the characteristics of this warm rain in the summers of 1998–2012 are analyzed. The results show that clear-sky pixels and “cold” pixels probably exist in some apparent warm-rain cases (60.5% and 11.2% of the time, respectively). According to this finding, warm-rain pixels are divided into pixels with and without clear sky. Statistical analysis shows that the existence of clear-sky pixels has a huge influence on the characteristics of the warm-rain pixels. The implications of this study are that many of the warm-rain cases are in fact not warm rain. When studying warm rain, the situation whereby the edges of pixels are clear sky should be fully considered. Also, when computing the weighted average brightness temperature and other characteristics of warm-rain pixels, parts that are clear-sky or cold pixels should be expelled to mitigate beam-filling problems.

scholarly journals Technical Note: 30 years of HIRS data of upper tropospheric humidity

2014 ◽  
Vol 14 (14) ◽  
pp. 7533-7541 ◽  
Author(s):  
K. Gierens ◽  
K. Eleftheratos ◽  
L. Shi
Keyword(s):  
High Resolution ◽  
Linear Regression ◽  
Statistical Significance ◽  
Technical Note ◽  
Regression Coefficients ◽  
Monthly Means ◽  
Data Set ◽  
Brightness Temperatures ◽  
Upper Tropospheric Humidity ◽  
The Mean

Abstract. We use 30 years of intercalibrated HIRS (High-Resolution Infrared Radiation Sounder) data to produce a 30-year data set of upper tropospheric humidity with respect to ice (UTHi). Since the required brightness temperatures (channels 12 and 6, T12 and T6) are intercalibrated to different versions of the HIRS sensors (HIRS/2 and HIRS/4) it is necessary to convert the channel 6 brightness temperatures which are intercalibrated to HIRS/4 into equivalent brightness temperatures intercalibrated to HIRS/2, which is achieved using a linear regression. Using the new regression coefficients we produce daily files of UTHi, T12 and T6, for each NOAA satellite and METOP-A (Meteorological Operational Satellite Programme), which carry the HIRS instrument. From this we calculate daily and monthly means in 2.5° × 2.5° resolution for the northern midlatitude zone 30–60° N. As a first application we calculate decadal means of UTHi and the brightness temperatures for the two decades 1980–1989 and 2000–2009. We find that the humidity mainly increased from the 1980s to the 2000s and that this increase is highly statistically significant in large regions of the considered midlatitude belt. The main reason for this result and its statistical significance is the corresponding increase of the T12 variance. Changes of the mean brightness temperatures are less significant.

scholarly journals Inter-calibrating SMMR brightness temperatures over continental surfaces

2020 ◽  
Author(s):  
Samuel Favrichon ◽  
Carlos Jimenez ◽  
Catherine Prigent
Keyword(s):  
Land Surface ◽  
Microwave Remote Sensing ◽  
Calibration Method ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Linear Correction ◽  
Long Time ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instrument such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the following Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, to the more recent Global Precipitation Mission Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long time series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 GHz and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ~6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from three years of GMI brightness temperatures are first calculated, and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data record of microwave brightness temperatures over continental surfaces.

scholarly journals Upper-tropospheric humidity changes under constant relative humidity

2015 ◽  
Vol 15 (20) ◽  
pp. 29497-29521
Author(s):  
K. Gierens ◽  
K. Eleftheratos
Keyword(s):  
Relative Humidity ◽  
Radiative Transfer ◽  
Water Vapour ◽  
Weighting Function ◽  
Scale Height ◽  
Weighted Mean ◽  
Constant Relative Humidity ◽  
Warming Climate ◽  
Upper Tropospheric Humidity ◽  
Peak Emission

Abstract. Theoretical derivations are given on the change of upper-tropospheric humidity (UTH) in a warming climate. Considered view is that the atmosphere, getting moister with increasing temperatures, will retain a constant relative humidity. In the present study we show that the upper-tropospheric humidity, a weighted mean over a relative humidity profile, will change in spite of constant relative humidity. The simple reason for this is that the weighting function, that defines UTH, changes in a moister atmosphere. Through analytical calculations using observations and through radiative transfer calculations we demonstrate that two quantities that define the weighting function of UTH can change: the water vapour scale height and the peak emission altitude. Applying these changes to real profiles of relative humidity shows that absolute UTH changes typically do not exceed 1 %. If larger changes would be observed they would be an indication of climatological changes of relative humidity. As such, an increase in UTH between 1980 and 2009 in the northern midlatitudes as shown by earlier studies using HIRS data, may be an indication of an increase in relative humidity as well.

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