The present paper deals with the retrieval of the atmospheric layer averaged relative humidity profiles using data from the Microwave Humidity Sounder (MHS) onboard the MetOp satellite. The retrieval has been innovatively performed by firstly retrieving humidity for pairs of thick overlapping layers (TOLs) used subsequently to derive humidity for associated thin isolated layer (TIL). A water vapour dependent (WVD) algorithm has been developed and applied to infer the humidity of TOLs. Thus, the retrieved profiles have been finally compared with standard algorithm (NORM). These algorithms have been developed based on radiative transfer simulations and study of sensitivities of MHS channels on humidity of various types of layers (TOL, TIL). The algorithm has been tested with MHS data and validated using concurrent radiosonde as well as NCEP reanalysis data indicating profile errors of ~15% and ~19%, respectively.
Being the strongest greenhouse gas, water vapor is the most important constituents in the Earth’s atmosphere, as its spatial and temporal variations affect various meteorological phenomena like formation of clouds, development of severe storms, and global warming [
Since Microwave Humidity Sounder (MHS) has three channels (described in next section) similar to SAPHIR; an algorithm has been developed for the retrieval of the atmospheric humidity profiles using MHS data that can also be used for retrieval from SAPHIR observations [
The present technique has been developed on the basis of simulated data for the MHS channels having frequencies around strong water vapour absorption band in microwave region of electromagnetic spectrum at 183.31 GHz (183.31 ± 1.0, 183.31 ± 3.0, and 183.31 + 7.0). To simulate the brightness temperature, the atmospheric fields have been taken from NCEP reanalysis for year 2009 to cover the spatial and temporal dynamic variability of relative humidity. Table
Statistics of the tropical atmosphere taken for simulation.
Parameters | Minimum | Maximum | Mean | Standard deviation |
---|---|---|---|---|
LARH (1000–550) hPa (%) | 5.54 | 95.26 | 28.61 | 13.80 |
LARH (1000–400) hPa (%) | 3.87 | 84.14 | 23.86 | 11.74 |
LARH (850–400) hPa (%) | 0.99 | 82.99 | 21.75 | 12.83 |
LARH (850–250) hPa (%) | 2.64 | 77.82 | 21.61 | 11.19 |
LARH (700–250) hPa (%) | 1.54 | 77.79 | 19.79 | 12.10 |
LARH (700–100) hPa (%) | 1.18 | 70.98 | 21.22 | 10.09 |
LARH (550–100) hPa (%) | 1.04 | 72.77 | 20.96 | 10.24 |
BT (182.31 GHz) (K) | 239.1 | 278.7 | 256.68 | 6.84 |
BT (180.31 GHz) (K) | 255.2 | 287.6 | 271.79 | 5.40 |
BT (190.31 GHz) (K) | 267.1 | 300.5 | 281.61 | 4.47 |
The algorithm has been tested on the brightness temperature data of MHS onboard METOP-A (available from EUMETSAT) for the last 10 days of each March, July, and December 2010 for the global tropical region (30°S–30°N). The results have been compared with the concurrent observations from radiosonde (Wyoming University) as well as NCEP analysis fields (1° spatial grid).
The retrieval algorithm for the atmospheric humidity profiles has been developed based on sensitivity analysis of the brightness temperature data simulated from scattering based microwave radiative transfer model (RTM) [
The sensitivity of simulated brightness temperatures on humidity has been studied under varying atmospheric conditions for various types of humidity layers taking into account that the weighting functions of MHS channels are very wide and highly overlapping. These weighting functions do not cover the near surface layers as well as the 250–100 hPa layer significantly. This experiment is useful in selecting the atmospheric TOLs influencing maximum number of channels to be considered for better retrievals.
Definitions of the TOL and TIL are given below.
LARH is the relative humidity (RH) averaged with respect to logarithm of pressure over a layer between two pressure limits “
Additionally, these TOLs are also innovatively utilized to derive humidity for TILs which otherwise will have large retrieval errors when directly derived from the channel brightness temperatures (due to broad overlapping nature of channel’s Weighting Functions (WFs)).
As mentioned above, TILs have been derived from two TOLs as follows.
From known LARH values for two TOLs with pressure levels “
The LARH for TIL between “
This approach of deriving TILs from TOLs is explained in Figures
Correlation coefficient between TOLs and TILs and brightness temperature of three MHS channels.
Channel frequency (GHz) | Correlation coefficient ( |
||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TOL | TIL | ||||||||||||
(1000–550) |
(1000–400) |
(850–400) |
(850–250) |
(700–250) |
(700–100) |
(550–100) |
(1000–850) |
(850–700) |
(700–550) |
(550–400) |
(400–250) |
(250–100) | |
190.31 | 0.6 | 0.7 | 0.7 | 0.8 | 0.7 | 0.5 | 0.5 | 0.3 | 0.4 | 0.6 | 0.7 | 0.5 | 0.2 |
180.31 | 0.4 | 0.6 | 0.7 | 0.9 | 0.9 | 0.8 | 0.8 | 0.1 | 0.3 | 0.6 | 0.8 | 0.9 | 0.4 |
182.31 | 0.6 | 0.8 | 0.8 | 0.9 | 0.9 | 0.7 | 0.6 | 0.1 | 0.4 | 0.7 | 0.8 | 0.7 | 0.2 |
Table
A typical example of variation of LARH with BT is given in Figure
Based on the nature of BT dependency on LARH as seen in Figure
(a) WVC = 0–8 gm/cm2. (b) WVC = 2–5 gm/cm2.
Since the RH can vary over a wide range irrespective of the total moisture content of the atmosphere, the trend of LARH with BT under limited WVC range between 2.0 g/cm2 and 5.0 g/cm2 is shown in Figure
Thus, (
In the present study, retrieval has been performed for the seven TOLs lying between the pressure values 1000–550 hPa, 1000–400 hPa, 850–400 hPa, 850–250 hPa, 700–250 hPa, 700–100 hPa, and 550–100 hPa, respectively, on the basis of their sensitivity with MHS channels’ brightness temperatures. From these seven TOLs, the LARH for six TILs lying between pressure values 1000–850 hPa, 850–700 hPa, 700–550 hPa, 550–400 hPa, 400–250 hPa, and 250–100 hPa has been derived. For implementing WVD algorithm, the chosen WVC ranges with minor overlaps are 0 to 3 g/cm2, 2 to 5 g/cm2, 4 to 7 g/cm2, and 6 to 10 g/cm2. The aggregate (overall or profile) retrieval error is defined here as root mean of sum of squares (RMSS) of root mean square (RMS) of differences of the two data sets.
Table
Testing of algorithms with overlapping layers.
Local inc. angle (degree) | Algorithm | WV range (g/cm2) | Retrieval errors of layer-average RH (RH in %) (simulated data = 12296) (overlapping layers) | Profile RMSS (%) | Aggr error (%) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1000–550 hPa | 1000–400 hPa | 850–400 hPa | 850–250 hPa | 700–250 hPa | 700–100 hPa | 550–100 hPa | |||||
0 | NORM | 0.0–10.0 | 11.82 | 8.00 | 8.30 | 4.50 | 4.00 | 6.23 | 6.76 | 7.50 | 7.50 |
WVD | 0.0–3.0 | 8.87 | 5.64 | 5.34 | 3.24 | 3.14 | 5.56 | 6.15 | 5.71 | 5.19 | |
2.0–5.0 | 7.47 | 4.92 | 5.72 | 3.08 | 3.60 | 5.93 | 6.61 | 5.53 | |||
4.0–7.0 | 6.30 | 4.70 | 5.21 | 3.32 | 3.55 | 5.68 | 6.46 | 5.16 | |||
6.0–10.0 | 4.93 | 3.69 | 3.89 | 2.82 | 3.78 | 4.68 | 5.40 | 4.25 | |||
|
|||||||||||
25 | NORM | 0.0–10.0 | 11.57 | 7.79 | 8.06 | 4.39 | 3.97 | 6.18 | 6.70 | 7.34 | 7.34 |
WVD | 0.0–3.0 | 8.75 | 5.57 | 5.35 | 3.23 | 3.14 | 5.53 | 6.09 | 5.66 | 5.20 | |
2.0–5.0 | 7.64 | 5.03 | 5.72 | 3.10 | 3.59 | 5.89 | 6.55 | 5.56 | |||
4.0–7.0 | 6.41 | 4.80 | 5.31 | 3.39 | 3.56 | 5.63 | 6.40 | 5.20 | |||
6.0–10.0 | 5.12 | 3.76 | 3.85 | 2.79 | 3.69 | 4.66 | 5.38 | 4.26 | |||
|
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50 | NORM | 0.0–10.0 | 11.25 | 7.54 | 7.61 | 4.21 | 3.88 | 5.97 | 6.48 | 7.09 | 7.09 |
WVD | 0.0–3.0 | 8.30 | 5.29 | 5.19 | 3.13 | 3.13 | 5.38 | 5.88 | 5.44 | 5.23 | |
2.0–5.0 | 8.32 | 5.52 | 5.88 | 3.24 | 3.56 | 5.73 | 6.34 | 5.74 | |||
4.0–7.0 | 6.78 | 5.21 | 5.77 | 3.65 | 3.77 | 5.44 | 6.15 | 5.36 | |||
6.0–10.0 | 5.54 | 4.02 | 3.98 | 2.61 | 3.34 | 4.53 | 5.21 | 4.28 | |||
|
|||||||||||
Swath performance | NORM | 7.31 | |||||||||
WVD | 5.60 | ||||||||||
Imp. (%) | 23.38 |
The algorithms have been first tested for TOL employing the retrieval coefficients for different incidence angles (Table
Testing of Algorithms with TIL.
Local inc. angle (degree) | MHS Algorithm | Retrieval errors of layer-average RH (RH in %) | Profile RMSS | |||||
---|---|---|---|---|---|---|---|---|
(simulated data = 12296) (isolated layers) | ||||||||
1000–850 hPa | 850–700 hPa | 700–550 hPa | 550–400 hPa | 400–250 hPa | 250–100 hPa | |||
0 | NORM | 14.94 | 16.71 | 12.25 | 6.06 | 6.19 | 12.83 | 12.19 |
WVD | 12.35 | 12.35 | 10.40 | 5.59 | 5.33 | 12.12 | 10.16 | |
|
||||||||
25 | NORM | 14.78 | 16.52 | 12.50 | 5.94 | 6.26 | 12.68 | 12.13 |
WVD | 12.38 | 12.40 | 10.56 | 5.49 | 5.32 | 12.01 | 10.18 | |
|
||||||||
50 | NORM | 14.93 | 16.11 | 12.30 | 5.81 | 6.44 | 12.35 | 11.98 |
WVD | 12.51 | 12.70 | 10.81 | 5.32 | 5.31 | 11.65 | 10.22 | |
|
||||||||
Swath performance | NORM | 12.10 | ||||||
WVD | 10.19 | |||||||
Imp. (%) | 15.83 |
From Tables
The brightness temperature data of MHS onboard METOP-A for March, July, and December 2010 for the tropical region over the entire globe has been taken to retrieve the seven TOLs from which six TILs have been derived using (
The comparison has been performed for the data of 2010 comprising of 10 days each from March, July, and December months. To collocate the radiosonde TIL with retrieved TIL from MHS, a search radius of 0.2° and ±1 hr temporal window is used. The scatter plot for each TIL retrieved from WVD algorithm and TIL from radiosonde observations is shown in Figures
Based on these collocated TILs from MHS and radiosonde, the root mean square difference (RMSD) and bias in retrieved TIL have been calculated and shown in Table
However, profile RMSS of RMSD is ~15%.
Dataset of retrieved TIL from MHS used for comparison with radiosonde has also been used to compare with TIL from NCEP reanalysis fields. The collocation of both datasets has been done by taking 1°
The RMSD and BIAS in retrieved TIL profiles for the year 2010.
Dataset | Isolated layers (hPa) | Bias (%) | Unbiased RMS difference (%) | RMS difference (MHS − RS) (%) | Profile RMSS of RMS difference (%) |
---|---|---|---|---|---|
Radiosonde (collocated points = 925) | 1000–850 | −5.02 | 14.82 | 15.64 | 15.24 |
850–700 | 0.93 | 18.12 | 18.13 | ||
700–550 | 6.07 | 14.79 | 15.98 | ||
550–400 | 4.75 | 10.14 | 11.19 | ||
400–250 | 3.00 | 9.47 | 9.93 | ||
250–100 | 10.92 | 14.88 | 18.45 | ||
|
|||||
NCEP | 1000–850 | 5.38 | 16.06 | 17.05 | 19.32 |
850–700 | 1.37 | 16.86 | 17.14 | ||
700–550 | −2.67 | 14.46 | 14.71 | ||
550–400 | −0.38 | 10.30 | 10.33 | ||
400–250 | 10.71 | 13.03 | 17.11 | ||
250–100 | 21.92 | 21.67 | 32.24 |
Here, also the RMSD is maximum (
From Table
A new technique for deriving improved layer averaged humidity profiles using microwave sounders has been developed and tested on actual MHS observations. Comparison of LARH profiles with those of radiosonde as well as NCEP indicates improved retrieval using present approach as compared to the standard algorithm. The algorithms developed in the present study are expected to improve further with more number of sounding channels like SAPHIR onboard Megha-Tropiques.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Authors would like to express their gratitude to Shri A. S. Kiran Kumar, Director of Space Applications Centre, Ahmedabad, for encouragement and guidance. Authors are also very thankful to Dr. J. S. Parihar, Deputy Director of Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area and Dr. P. K. Pal, Group Director of Atmospheric Oceanic Sciences Group for their keen interest and support.