Viju O. John

Current changes in tropical precipitation.

Current changes in tropical precipitation from satellite data and climate models are assessed. Wet and dry regions of the tropics are defined as the highest 30% and lowest 70% of monthly precipitation values. Observed tropical ocean trends in the wet regime (1.8%/decade) and the dry regions (−2.6%/decade) according to the Global Precipitation Climatology Project (GPCP) over the period including Special Sensor Microwave Imager (SSM/I) data (1988–2008), where GPCP is believed to be more reliable, are of smaller magnitude than when including the entire time series (1979–2008) and closer to model simulations than previous comparisons. Analysing changes in extreme precipitation using daily data within the wet regions, an increase in the frequency of the heaviest 6% of events with warming for the SSM/I observations and model ensemble mean is identified. The SSM/I data indicate an increased frequency of the heaviest events with warming, several times larger than the expected Clausius–Clapeyron scaling and at the upper limit of the substantial range in responses in the model simulations.

Intercomparison of general purpose clear sky atmospheric radiative transfer models for the millimeter/submillimeter spectral range

[1] We compare a number of radiative transfer models for atmospheric sounding in the millimeter and submillimeter wavelength range, check their consistency, and investigate their deviations from each other. This intercomparison deals with three different aspects of radiative transfer models: (1) the inherent physics of gaseous absorption lines and how they are modeled, (2) the calculation of absorption coefficients, and (3) the full calculation of radiative transfer for different geometries, i.e., up-looking, down-looking, and limblooking. The correctness and consistency of the implementations are tested by comparing calculations with predefined input such as spectroscopic data, line shape, continuum absorption model, and frequency grid. The absorption coefficients and brightness temperatures calculated by the different models are generally within about 1% of each other. Furthermore, the variability or uncertainty of the model results is estimated if (except for the atmospheric scenario) the input such as spectroscopic data, line shape, and continuum absorption model could be chosen freely. Here the models deviate from each other by about 10% around the center of major absorption lines. The main cause of such discrepancies is the variability of reported spectroscopic data for line absorption and of the continuum absorption model. Further possible causes of discrepancies are different frequency and pressure grids and differences in the corresponding interpolation routines, as well as differences in the line shape functions used, namely a prefactor of (n/n0 )o r (n/n0) 2 of the Van-Vleck-Weisskopf line shape function. Whether or not the discrepancies affect retrieval results remains to be investigated for each application individually.

Scan asymmetries in AMSU-B data

A simple method of averaging measurements for different scan positions was used to quantify scan asymmetries in AMSU-B brightness temperatures for the sensors on the satellites NOAA 15, 16, and 17. ...

On the Importance of Vaisala RS92 Radiosonde Humidity Corrections for a Better Agreement between Measured and Modeled Satellite Radiances

Astudyhasbeencarriedouttoassesstheimportanceofradiosondecorrectionsinimprovingtheagreement between satellite and radiosonde measurements of upper-tropospheric humidity. Infrared [High Resolution Infrared Radiation Sounder (HIRS)-12] and microwave [Advanced Microwave Sounding Unit (AMSU)-18] measurements from the NOAA-17 satellite were used for this purpose. The agreement was assessed by comparing the satellite measurements against simulated measurements using collocated radiosonde profiles of the Atmospheric Radiation Measurement (ARM) Program undertaken at tropical and midlatitude sites. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate the satellite radiances. The comparisons have been done under clear-sky conditions, separately for daytime and nighttime soundings. Only Vaisala RS92 radiosonde sensors were used and an empirical correction (EC) was applied to the radiosonde measurements. The EC includes correction for mean calibration bias and for solar radiation error, anditremovesradiosondebiasrelativetothreeinstrumentsofknownaccuracy.Forthenighttimedataset,the EC significantly reduces the bias from 0.63 to 20.10 K in AMSU-18 and from 1.26 to 0.35 K in HIRS-12. The EC has an even greater impact on the daytime dataset with a bias reduction from 2.38 to 0.28 K in AMSU-18 and from 2.51 to 0.59 K in HIRS-12. The present study promises a more accurate approach in future radiosondebased studies in the upper troposphere.

Comparing upper tropospheric humidity data from microwave satellite instruments and tropical radiosondes

[1] Atmospheric humidity plays an important role in the Earth’s climate. Microwave satellite data provide valuable humidity observations in the upper troposphere with global coverage. In this study, we compare upper tropospheric humidity (UTH) retrieved from the Advanced Microwave Sounding Unit and the Microwave Humidity Sounder against radiosonde data measured at four of the central facilities of the Atmospheric Radiation Measurement program. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate satellite brightness temperatures from the radiosonde profiles. Strong ice clouds were filtered out, as their influence on microwave measurements leads to incorrect UTH values. Day and night radiosonde profiles were analyzed separately to take into account the radiosonde radiation bias. The comparison between radiosonde and satellite is most meaningful for data in cloud‐free, nighttime conditions and with a time difference of less than 2 hr. We found good agreement between the two data sets. The satellite data were slightly moister than the radiosonde data, with a mean difference of 1%–2.3% relative humidity (RH), depending on the radiosonde site. Monthly gridded data were also compared and showed a slightly larger mean difference of up to 3.3% RH, which can be explained by sampling issues.

A cautionary note on the use of Gaussian statistics in Satellite-based UTH climatologies

This letter presents a cautionary note on the assumption of Gaussian behavior for upper tropospheric humidity (UTH) derived from satellite data in climatological studies, which can introduce a wet bias in the climatology. An example study using European Centre for Medium-Range Weather Forecasts reanalysis data shows that this wet bias can reach up to 6 %RH, which is significant for climatological applications. A simple Monte Carlo approach demonstrates that these differences and their link to the variability of brightness temperatures are due to a log-normal distribution of the UTH. This problem can be solved by using robust estimators such as the median instead of the arithmetic mean.

Variability of Indian summer monsoon in a new upper tropospheric humidity data set

Using a new data set we demonstrate the variability of upper troposphere humidity (UTH) associated with the Indian Summer Monsoon (ISM). The main advantage of the new data set is its all-sky repres ...

An Assessment of SAPHIR Calibration Using Quality Tropical Soundings

AbstractThe Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) instrument on board the Megha-Tropiques (MT) platform is a cross-track, multichannel microwave humidity sounder with six channels near the 183.31-GHz water vapor absorption line, a maximum scan angle of 42.96° (resulting in a maximum incidence angle of 50.7°), a 1700-km-wide swath, and a footprint resolution of 10 km at nadir. SAPHIR L1A2 brightness temperature (BT) observations have been compared to BTs simulated by the radiative transfer model (RTM) Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV-10), using in situ measurements from radiosondes as input. Selected radiosonde humidity observations from the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year (CINDY)–Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign (September 2011–March 2012) were spatiotemporally collocated with MT overpasses. Although several...

Intercalibrating Microwave Satellite Observations for Monitoring Long-Term Variations in Upper- and Midtropospheric Water Vapor*

AbstractThis paper analyzes the growing archive of 183-GHz water vapor absorption band measurements from the Advanced Microwave Sounding Unit B (AMSU-B) and Microwave Humidity Sounder (MHS) on board polar-orbiting satellites and document adjustments necessary to use the data for long-term climate monitoring. The water vapor channels located at 183.31 ± 1 GHz and 183.31 ± 3 GHz are sensitive to upper- and midtropospheric relative humidity and less prone to the clear-sky sampling bias than infrared measurements, making them a valuable but underutilized source of information on free-tropospheric water vapor. A method for the limb correction of the satellite viewing angle based upon a simplified model of radiative transfer is introduced to remove the scan angle dependence of the radiances. Biases due to the difference in local observation time between satellites and spurious trends associated with satellite orbital drift are then diagnosed and adjusted for using synthetic radiative simulations based on the In...

Evaluating Instrumental Inhomogeneities in Global Radiosonde Upper Tropospheric Humidity Data Using Microwave Satellite Data

In this paper, the overall quality of the water vapor profiles of global operational radiosonde data for the period 2000-2009 is investigated using upper tropospheric humidity (UTH) retrieved from microwave satellite data. Overall, the nighttime radiosonde data showed a dry bias (- 5% to -15%) over Europe, Australia, and New Zealand and systematically moist bias (greater than 30%) over China and the former Soviet Union. The nighttime sonde data from the U.S. and Canada showed a bias between -10% and 20%. Most stations indicated a daytime radiation dry bias, except for a few stations from the U.S. and the former Soviet Union. A sensorwise comparison showed a large nighttime wet bias for the Russian (MRZ-3A and MARS) and Chinese GZZ-2 sensors, a relatively small nighttime wet bias for the U.S. Sippican and VIZ-B2 sensors, and a nighttime dry bias for the Chinese GTS1, Vaisala (RS80-A, RS80-H, RS90, RS92K, and RS92-SGP), and the U.S. VIZ-MKII sensors. All sensors had a daytime radiation dry bias, except for the Russian MRZ-3A sensor that had a daytime radiation wet bias that could be because of the daytime radiation bias correction. Because of the large differences between different radiosonde sensors, it is essential for UTH studies to only use the data measured using a single type of sensor at any given station.