Upper-tropospheric Humidity Research Articles

Is there a stratospheric radiative feedback in global warming simulations?

The radiative impacts of the stratosphere in global warming simulations are investigated using abrupt CO2 quadrupling experiments of the Coupled Model Inter-comparison Project phase 5 (CMIP5), with a focus on stratospheric temperature and water vapor. It is found that the stratospheric temperature change has a robust bullhorn-like zonal-mean pattern due to a strengthening of the stratospheric overturning circulation. This temperature change modifies the zonal mean top-of-the-atmosphere energy balance, but the compensation of the regional effects leads to an insignificant global-mean radiative feedback (−0.02 ± 0.04 W m−2 K−1). The stratospheric water vapor concentration generally increases, which leads to a weak positive global-mean radiative feedback (0.02 ± 0.01 W m−2 K−1). The stratospheric moistening is related to mixing of elevated upper-tropospheric humidity, and, to a lesser extent, to change in tropical tropopause temperature. Our results indicate that the strength of the stratospheric water vapor feedback is noticeably larger in high-top models than in low-top ones. The results here indicate that although its radiative impact as a forcing adjustment is significant, the stratosphere makes a minor contribution to the overall climate feedback in CMIP5 models.

Validation of Upper-Tropospheric Humidity from SAPHIR on board Megha-Tropiques Using Tropical Soundings

AbstractThis paper describes the upper-tropospheric humidity (UTH) product derived from brightness temperature measurements of the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) radiometer on board the Megha-Tropiques satellite. Under nonscattering conditions, the observations from three channels of SAPHIR—located at ±0.2, ±1.1, and ±2.8 GHz, respectively, around the 183.31-GHz strong water vapor absorption band—are interpreted into three different UTHs following a well-established method and thus describing the humidity content of the upper to midtroposphere. The evaluation of the UTHs is performed using reference UTHs defined from relative humidity (RH) profiles from radiosoundings of two field campaigns: the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011/Dynamics of the Madden–Julian Oscillation/Atmospheric Radiation Measurement Program Madden–Julian Oscillation Investigation Experiment (CINDY/DYNAMO/AMIE) and a Megha-Tropiques dedicated campaign in Ouagadougou, Burkina Faso, during the summer of 2012. A budget of the various uncertainties associated with each component of the evaluation method (such as the radiometric sensitivity and the radiative transfer computations) was created to achieve a more robust comparison between the two UTH estimates. The comparison between the reference UTHs and the SAPHIR UTHs reveals small global biases of lower than 2% RH on average, with correlation coefficients between 0.86 and 0.89. Taking into account the individual uncertainties gives root-mean-square errors of regressions that range between 0.92% and 4.71%. These three UTHs provide a vertical distribution of the RH that is suitable for studying various temporal and spatial scales of the tropical variability. The signature of a mesoscale convective system on its environment is briefly presented to illustrate the capability of this new dataset.

Global-scale atmosphere monitoring by in-service aircraft – current achievements and future prospects of the European Research Infrastructure IAGOS

The European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System) operates a global-scale monitoring system for atmospheric trace gases, aerosols and clouds utilising the existing global civil aircraft. This new monitoring infrastructure builds on the heritage of the former research projects MOZAIC (Measurement of Ozone and Water Vapour on Airbus In-service Aircraft) and CARIBIC (Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container). CARIBIC continues within IAGOS and acts as an important airborne measurement reference standard within the wider IAGOS fleet. IAGOS is a major contributor to the in-situ component of the Copernicus Atmosphere Monitoring Service (CAMS), the successor to the Global Monitoring for the Environment and Security – Atmospheric Service, and is providing data for users in science, weather services and atmospherically relevant policy. IAGOS is unique in collecting regular in-situ observations of reactive gases, greenhouse gases and aerosol concentrations in the upper troposphere and lowermost stratosphere (UTLS) at high spatial resolution. It also provides routine vertical profiles of these species in the troposphere over continental sites or regions, many of which are undersampled by other networks or sampling studies, particularly in Africa, Southeast Asia and South America. In combination with MOZAIC and CARIBIC, IAGOS has provided long-term observations of atmospheric chemical composition in the UTLS since 1994. The longest time series are 20 yr of temperature, H2O and O3, and 9–15 yr of aerosols, CO, NOy, CO2, CH4, N2O, SF6, Hg, acetone, ~30 HFCs and ~20 non-methane hydrocarbons. Among the scientific highlights which have emerged from these data sets are observations of extreme concentrations of O3 and CO over the Pacific basin that have never or rarely been recorded over the Atlantic region for the past 12 yr; detailed information on the temporal and regional distributions of O3, CO, H2O, NOy and aerosol particles in the UTLS, including the impacts of cross-tropopause transport, deep convection and lightning on the distribution of these species; characterisation of ice-supersaturated regions in the UTLS; and finally, improved understanding of the spatial distribution of upper tropospheric humidity including the finding that the UTLS is much more humid than previously assumed.

Technical Note: Reanalysis of upper troposphere humidity data from the MOZAIC programme for the period 1994 to 2009

Abstract. In situ observational data on the relative humidity (RH) in the upper troposphere and lowermost stratosphere (UT/LS), or tropopause region, collected aboard civil passenger aircraft in the MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) programme were reanalysed for the period 2000 to 2009. Previous analyses of probability distribution functions (PDFs) of upper troposphere humidity (UTH) data from MOZAIC observations from year 2000 and later indicated a bias of UTH data towards higher RH values compared to data of the period 1994 to 1999. As a result, the PDF of UTH data show a substantial fraction of observations above 100% relative humidity with respect to liquid water. Such supersaturations, however, do not occur in the atmosphere because there is always a sufficient number of condensation nuclei available, that trigger condensation as soon as liquid saturation is slightly exceeded. An in-depth reanalysis of the data set identified a coding error in the calibration procedure from year 2000 on. The error did not affect earlier data from 1994 to 1999. The full data set for 2000–2009 was reanalysed applying the corrected calibration procedure. Applied correction schemes and a revised error analysis are presented along with the reanalysed PDF of relative humidity with respect to liquid water (RHliquid) and ice (RHice).

Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass

Abstract. Retrievals of cloud ice mass and humidity from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and the Odin-SMR (Sub-Millimetre Radiometer) limb sounder are presented and example applications of the data are given. SMILES data give an unprecedented view of the diurnal variation of cloud ice mass. Mean regional diurnal cycles are reported and compared to some global climate models. Some improvements in the models regarding diurnal timing and relative amplitude were noted, but the models' mean ice mass around 250 hPa is still low compared to the observations. The influence of the ENSO (El Niño–Southern Oscillation) state on the upper troposphere is demonstrated using 12 years of Odin-SMR data. The same retrieval scheme is applied for both sensors, and gives low systematic differences between the two data sets. A special feature of this Bayesian retrieval scheme, of Monte Carlo integration type, is that values are produced for all measurements but for some atmospheric states retrieved values only reflect a priori assumptions. However, this "all-weather" capability allows a direct statistical comparison to model data, in contrast to many other satellite data sets. Another strength of the retrievals is the detailed treatment of "beam filling" that otherwise would cause large systematic biases for these passive cloud ice mass retrievals. The main retrieval inputs are spectra around 635/525 GHz from tangent altitudes below 8/9 km for SMILES/Odin-SMR, respectively. For both sensors, the data cover the upper troposphere between 30° S and 30° N. Humidity is reported as both relative humidity and volume mixing ratio. The vertical coverage of SMILES is restricted to a single layer, while Odin-SMR gives some profiling capability between 300 and 150 hPa. Ice mass is given as the partial ice water path above 260 hPa, but for Odin-SMR ice water content, estimates are also provided. Besides a smaller contrast between most dry and wet cases, the agreement with Aura MLS (Microwave Limb Sounder) humidity data is good. In terms of tropical mean humidity, all three data sets agree within 3.5 %RHi. Mean ice mass is about a factor of 2 lower compared to CloudSat. This deviation is caused by the fact that different particle size distributions are assumed, combined with saturation and a priori influences in the SMILES and Odin-SMR data.

Evaluation of operational INSAT-3D UTH product, using Radiosonde, Meteosat-7 and NCEP Analysis

Abstract. Recently available satellite observations from the water vapor channel (6.5–7.1 μm) of the Imager on-board India's geostationary satellite, INSAT-3D have been used to estimate Upper Tropospheric Humidity (UTH). In this study, operationally retrieved UTH product has been compared and validated for the period of Jan–Jun, 2014, using in-situ and satellite measurements. In-situ measurements of UTH have been indirectly derived using humidity profiles obtained from a network of radiosonde stations from NOAA/ESRL database. Meteosat-7 UTH products have been used as satellite measurements. The validation of INSAT-3D UTH against UTH derived from radiosonde profiles shows reasonable agreement, with linear correlation coefficients ranging from 0.78 to 0.87 and the slope of the regression line ranging from 0.52 to 0.77. The UTH tends to overestimate observed humidity by ~4 % with RMS difference of ~12 %. Comparison of INSAT-3D UTH product with Meteosat-7 UTH product suggests a good match with RMS difference of 7.61% and a mean bias of −0.43 %, linear correlation coefficients varying from 0.88 to 0.93 and slope of the regression line varying from 0.64 to 1.08. The UTH products from INSAT-3D and Meteosat-7 have also been inter-compared by validating the two against the UTH derived from a set of collocated radiosonde observations. INSAT-3D UTH shows a RMSD of 10.65 % and bias of 0.78 % which matches very well with Meteosat-7 UTH with a RMSD of 10.31 % and bias of −0.53 %.

Diagnosing the average spatio-temporal impact of convective systems – Part 2: A model intercomparison using satellite data

Abstract. The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data. The models tend to over-predict the occurrence of rain rates that are less than ≈ 3 mm h−1 compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase. Despite having a larger climatological mean upper-tropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location. Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

Leveraging Highly Accurate Data in Diagnosing Errors in Atmospheric Models

Highly accurate data can serve the numerical weather prediction, climate prediction, and atmospheric reanalysis communities by better enabling the diagnosis of model error through the careful examination of the diagnostics of data assimilation, especially the firstguess departures and the analysis increments. The highly accurate data require no bias correction for instrument error, leaving the possibility of confusion with error in forward models for observations as the lone hindrance to the diagnosis of model error. With this scenario in mind, we conducted numerical experiments to investigate the potential confusion using the data assimilation system at the European Centre for Medium-Range Weather Forecasts. We found that large-scale systematic model error can be misattributed to error in the forward models for observations, thereby reducing systematic firstguess departures and impeding the mitigation of model error. The same large-scale model error generated a 20% increase in analyzed specific humidity near the tropopause, suggesting that current observational data cannot constrain the upper tropospheric humidity in current models, which contributes substantially to greenhouse forcing of the climate. We expect that the confusion of model error for an error in the forward models for observations occurs regardless of the objective method used to diagnose model error. Our findings underline the importance for continued improvement in radiative transfer calculations and highlight the value of multiple sources of accurate data that are redundant in their sensitivity to atmospheric variables yet orthogonal in their radiation physics.

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

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.

Convective Characteristics of the Madden–Julian Oscillation over the Central Indian Ocean Observed by Shipborne Radar during DYNAMO

Abstract This study investigates the convective population and environmental conditions during three MJO events over the central Indian Ocean in late 2011 using measurements collected from the Research Vessel (R/V) Roger Revelle deployed in Dynamics of the MJO (DYNAMO). Radar-based rainfall estimates from the Revelle C-band radar are first placed in the context of larger-scale Tropical Rainfall Measuring Mission (TRMM) rainfall data to demonstrate that the reduced Revelle radar range captured the MJO convective evolution. Time series analysis and MJO phase-based composites of Revelle measurements both support the “recharge–discharge” MJO theory. Time series of echo-top heights indicate that convective deepening during the MJO onset occurs over a 12–16-day period. Composite statistics show evident recharging–discharging features in convection and the environment. Population of shallow/isolated convective cells, SST, CAPE, and the lower-tropospheric moisture increase (recharge) substantially approximately two to three phases prior to the MJO onset. Deep and intense convection and lightning peak in phase 1 when the sea surface temperature and CAPE are near maximum values. However, cells in this phase are not well organized and produce little stratiform rain, possibly owing to reduced shear and a relatively dry upper troposphere. The presence of deep convection leads the mid- to upper-tropospheric humidity by one to two phases, suggesting its role in moistening these levels. During the MJO onset (i.e., phase 2), the mid- to upper troposphere becomes very moist, and precipitation, radar echo-top heights, and the mesoscale extent of precipitation all increase and obtain peak values. Persistent heavy precipitation in these active periods helps reduce the SST and dry/stabilize (or discharge) the atmosphere.

A Qualitative study of Some Meteorological Features During tropical Cyclone PHET Using Satellite Observations and WRF Modeling System

The satellite derived meteorological parameters are quite useful for understanding the genesis of a tropical cyclone. This paper analyses some of the characteristic features of the tropical cyclone (TC) PHET using satellite derived meteorological observations, and numerical model simulations while investigating the performance of various cumulus parameterization schemes using Weather Research and Forecasting (WRF) modeling system. The genesis of the TC is primarily discussed using the observed meteorological parameters including the outgoing long-wave radiation, quantitative precipitation estimate (or rainfall), sea surface temperature, relative vorticity and upper tropospheric humidity. These satellite derived parameters show suitable meteorological condition for the development and propagation of the TC. The qualitative analysis of WRF simulated results indicates that Kain-Fritsch cumulus scheme (Kain and Fritsch, 1990 and 1993; Kain, 2004) performs relatively better in predicting various parameters in relation to the genesis and propagation of PHET.

Diagnosing the average spatio-temporal impact of convective systems – Part 1: A methodology for evaluating climate models

Abstract. An earlier method to determine the mean response of upper-tropospheric water to localised deep convective systems (DC systems) is improved and applied to the EC-Earth climate model. Following Zelinka and Hartmann (2009), several fields related to moist processes and radiation from various satellites are composited with respect to the local maxima in rain rate to determine their spatio-temporal evolution with deep convection in the central Pacific Ocean. Major improvements to the earlier study are the isolation of DC systems in time so as to prevent multiple sampling of the same event, and a revised definition of the mean background state that allows for better characterisation of the DC-system-induced anomalies. The observed DC systems in this study propagate westward at ~4 m s−1. Both the upper-tropospheric relative humidity and the outgoing longwave radiation are substantially perturbed over a broad horizontal extent and for periods >30 h. The cloud fraction anomaly is fairly constant with height but small maximum can be seen around 200 hPa. The cloud ice water content anomaly is mostly confined to pressures greater than 150 hPa and reaches its maximum around 450 hPa, a few hours after the peak convection. Consistent with the large increase in upper-tropospheric cloud ice water content, albedo increases dramatically and persists about 30 h after peak convection. Applying the compositing technique to EC-Earth allows an assessment of the model representation of DC systems. The model captures the large-scale responses, most notably for outgoing longwave radiation, but there are a number of important differences. DC systems appear to propagate eastward in the model, suggesting a strong link to Kelvin waves instead of equatorial Rossby waves. The diurnal cycle in the model is more pronounced and appears to trigger new convection further to the west each time. Finally, the modelled ice water content anomaly peaks at pressures greater than 500 hPa and in the upper troposphere between 250 hPa and 500 hPa, there is less ice than the observations and it does not persist as long after peak convection. The modelled upper-tropospheric cloud fraction anomaly, however, is of a comparable magnitude and exhibits a similar longevity as the observations.

Comparison of tropical and midlatitude thunderstorm characteristics anchored in thermodynamic and dynamic aspects

Thunderstorms prevailing over tropics and midlatitudes depict dissimilar features relating to the thermodynamic and dynamic aspects. The identification of the physical characteristics of the tropical and midlatitude thunderstorms is the main objective of the present study. The stations Kolkata (22.6°N, 88.4°E) and Denver (39.47°N, 104.32°W) are selected from the tropics and midlatitudes for the comparative analyses. The study reveals that the average storm relative helicity (SRH) and the lapse rate between 700 and 500 hPa level is much higher over Denver compared to Kolkata during thunderstorm days. The study further reveals that the surface to mid troposphere (upto 500 hPa) become drier (∼2 times) over Denver than Kolkata prior to the occurrence of thunderstorms while the upper tropospheric (300–100 hPa) humidity remains comparable for both the locations.

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.

An assessment of the diurnal variation of upper tropospheric humidity in reanalysis data sets

AbstractDiurnal variations of upper tropospheric humidity (UTH) in five different reanalysis data sets are compared over convective land and ocean regions and evaluated using multiple satellite observations as a reference. All reanalysis data sets reproduce the day‐night contrast of upper tropospheric humidity and the land‐ocean contrast in the diurnal amplitude. The infrared satellite measurements indicate a slightly later diurnal minimum over land relative to most reanalyses and the microwave satellite measurements, suggesting that cloud masking of the infrared radiances may introduce a small (∼3 h) bias in the phase. One reanalysis exhibits a substantially different diurnal cycle over land which is inconsistent with both infrared and microwave satellite measurements and other reanalysis products. This product also exhibits a different covariance between vertical velocity, cloud water, and humidity than other reanalyses, suggesting that the phase bias is related to deficiencies in the parameterization of moist convective processes.

Land Surface Albedo from Geostationary Satelites: A Multiagency Collaboration within SCOPE-CM

Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.

The representation of tropical upper tropospheric water in EC Earth V2

Tropical upper tropospheric humidity, clouds, and ice water content, as well as outgoing longwave radiation (OLR), are evaluated in the climate model EC Earth with the aid of satellite retrievals. The Atmospheric Infrared Sounder and Microwave Limb Sounder together provide good coverage of relative humidity. EC Earth's relative humidity is in fair agreement with these observations. CloudSat and CALIPSO data are combined to provide cloud fractions estimates throughout the altitude region considered (500-100 hPa). EC Earth is found to overestimate the degree of cloud cover above 200 hPa and underestimate it below. Precipitating and non-precipitating EC Earth ice definitions are combined to form a complete ice water content. EC Earth's ice water content is below the uncertainty range of CloudSat above 250 hPa, but can be twice as high as CloudSat's estimate in the melting layer. CERES data show that the model underestimates the impact of clouds on OLR, on average with about 9 W m(-2). Regionally, EC Earth's outgoing longwave radiation can be similar to 20 W m(-2) higher than the observation. A comparison to ERA-Interim provides further perspectives on the model's performance. Limitations of the satellite observations are emphasised and their uncertainties are, throughout, considered in the analysis. Evaluating multiple model variables in parallel is a more ambitious approach than is customary.

Evaluation of upper tropospheric humidity from NCEP analysis and WRF Model Forecast with Kalpana observation during Indian summer monsoon 2010

AbstractThis paper presents an evaluation of upper tropospheric humidity (UTH) fields from National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis and Weather Research and Forecasting (WRF) mesoscale model forecast with satellite derived UTH. For this purpose, UTH (weighted average of relative humidity in 200–500 hPa layer) obtained from Indian geostationary satellite Kalpana is compared with the UTH computed from NCEP analysis and WRF model forecast during the summer monsoon, 2010. Kaplana‐derived products are not included in the NCEP analysis: therefore, a comparison of the two independent datasets is promising. This study indicates that UTH derived from different sources (Kalpana UTH, NCEP Analysed and subsequent model forecasted UTH) matches very well over the Indian region. However, there are region‐specific small departures in UTH which increases the model predicted UTH. Copyright © 2012 Royal Meteorological Society

Monsoons: prediction, variability and impact

It is estimated that more than 60% of the world's population depends heavily on monsoon rainfall for their subsistence. While a poor monsoon season can trigger food crisis and have a negative impact on a country's economy, an exceptional monsoon can have disastrous consequences on people's lives and property due to floods and landslides triggered by torrential rains. Even small variations in rainfall relative to the average expected monsoon rainfall can have a dramatic effect. Crop yields and water resources are controlled by the cycles of monsoon circulation and it is of no surprise that analysis of monsoon variability and monsoon prediction on monthly to seasonal time scales are extremely important scientific activities. Accurate forecasts of daily precipitation during the monsoon season are also essential in order to issue effective warnings of severe weather events. This special issue assembles a series of articles on the state of the art scientific understanding of prediction, variability and the impact of monsoons. The first set of papers (Agnihotri and Mohapatra, Sardar et al., and P. Kumar et al.) looks at daily and monthly forecasts of monsoon rainfall, as well as forecasts of upper tropospheric humidity fields. The sensitivity to different convection schemes is assessed comparing monthly rainfall forecasts to satellite derived observations. The understanding of forecast uncertainty linked to different parameterization of convection is crucial for a better representation of physical processes in numerical weather prediction models and thus better predictions. The use of multi-model ensembles in order to account for forecast uncertainty has become increasingly popular in current scientific practice and this special issue includes four papers on the topic (A. Kumar et al., Goswami et al., Singh et al. and Samala et al.). The performance of a simple ensemble mean and a weighted average, whereby weights are calculated using neural network concepts, are discussed. Moreover, the improvements of post-processed model output using a Canonical Correlation Analysis (CCA) is compared to direct model output. The multi-model ensemble is also used to examine weaknesses in forecasts of circulation anomalies during weak Indian monsoon years. The next set of papers (Ha et al., Mujumdar et al. and Rajeevan et al.) includes a review of the multi-scale variability of the East Asian monsoon, where possible links between climate change and monsoon variability are explored. The variability of the Indian monsoon is looked at in relation to La Niña. Moreover, the variability of the Northeast monsoon over India is discussed together with its predictability. The final collection of papers describes droughts resulting from changes in the monsoon regimes in South America and India (Coelho et al. and M. Naresh Kumar et al.). In the case of South America, the large-scale oceanic and atmospheric conditions are discussed as possible mechanisms associated with drought conditions. The analysis of time series of the Standardized Precipitation Index for India has shown that the drought frequency has increased in recent years. There is no doubt that further scientific research on monsoon processes and variability, and their predictability and impacts, is still urgently required. The complexity of physical processes and atmospheric forcings renders the task of understanding monsoons challenging for the scientific community, and this special issue is a contribution towards this effort.

Intersatellite bias of the high-resolution infrared radiation sounder water vapor channel determined using ISCCP B1 data

Information on the distribution and transport of upper tropospheric humidity helps our understanding of atmospheric circulation. However, efforts to develop a dataset that is not diurnally biased-that is, one that is based on geostationary data-have been hindered by a lack of intercalibration of the historical geostationary satellite water vapor channels. Toward this end, the high-resolution infrared radiation sounder (HIRS) intersatellite bias is determined by using collocated observations of geostationary and HIRS to compute monthly biases between HIRS instruments on different satellites. The resulting HIRS intersatellite bias then allows the construction of a temporally consistent HIRS record, from which geostationary satellite water vapor channels can be intercalibrated. This intercalibration will allow a uniform analysis of upper tropospheric water vapor.