Atmospheric Infrared Sounder Temperature Research Articles

Convective gravity wave events during summer near 54° N, present in both AIRS and Rayleigh–Mie–Raman (RMR) lidar observations

Abstract. We connect tropospheric deep convective events over western Europe, as measured by the 8.1 µm radiance observations from the Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite, to horizontal brightness temperature variance in the 4.3 µm AIRS channel (maximum sensitivity at around 40 km) and temperature perturbations in vertical lidar profiles (between 33-43 km) over Kühlungsborn, Germany (54.12∘ N, 11.77∘ E). Although the lidar and AIRS are sensitive to different parts of the gravity wave spectrum, they both capture the same peaks in gravity wave activity tied to convection. This suggests that a broad range of vertical wavelengths is present in the convective gravity waves. To account for wave propagation conditions from the troposphere to the stratosphere, we also consider the horizontal winds in the troposphere and stratosphere using the ECMWF Integrated Forecasting System (IFS) operational analysis. In this work, we highlight sporadic peaks in gravity wave activity in summer greatly exceeding those typical of summer, which is generally a season with lower wave activity compared to winter. Although these events are present in roughly half of the years (between 2003 and 2019), we focus our study on two case study years (2014 and 2015). These case study years were chosen because of the high cadence of lidar soundings close in time to the convective events. These events, while sporadic, could contribute significantly to the zonal mean momentum budget and are not accounted for in weather and climate models.

A Warmer and Wetter Arctic: Insights From a 20‐Years AIRS Record

AbstractArctic sea ice has undergone significant change in areal coverage, thickness, ice type since the 1980s and more recently since the early 2000s, where a “New Arctic” regime now exists. Since the sea ice modulates exchanges of energy from the ocean to the atmosphere, this changing sea ice environment has profound effects on the local climate. However, due to the Arctic's remote location, wide‐spread and long‐term data records of the atmosphere are few and far between. The Atmospheric Infrared Sounder (AIRS) onboard NASA's Aqua satellite was launched in May 2002 has been collecting twice daily, global data of the Earth's temperature and humidity for over 20 years. We use AIRS temperature and humidity data to investigate relationships between the sea ice, and surface and atmospheric conditions between 2003 and 2022. The Arctic atmosphere is becoming warmer and wetter with sea ice loss and the change is most pronounced near the surface. Strongest correlations occur in the fall when the surface and lower atmosphere are tightly coupled. When comparing the first (2003–2012) and last (2013–2022) decade of the New Arctic, results show that the warming and moistening is slowing down as the sea ice regime and sea ice loss has stabilized in 2013–2022. Cooling and drying is occurring in winter in the Barents and other peripheral seas in the last decade possibly due to a negative feedback loop, where winter sea ice regrowth is occurring at a faster pace. This work highlights the importance of sea ice atmosphere interactions and long‐term climate data records, specifically in remote and drastically changing places like the Arctic.

Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic

The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.

Inter-Comparison of AIRS Temperature and Relative Humidity Profiles with AMMA and DACCIWA Radiosonde Observations over West Africa

The vertical profiles of temperature and water vapour from the Atmospheric InfraRed Sounder (AIRS) have been validated across various regions of the globe as an effort to provide a substitute for radiosonde observations. However, there is a paucity of inter-comparisons over West Africa where local convective processes dominate and radiosonde observations (RAOBs) are limited. This study validates AIRS temperature and relative humidity profiles for selected radiosonde stations in West Africa. Radiosonde data were obtained from the AMMA and DACCIWA campaigns which spanned 2006–2008 and June–July 2016 respectively and offered a period of prolonged radiosonde observations in West Africa. AIRS performance was evaluated with the bias and root mean square difference (RMSD) at seven RAOB stations which were grouped into coastal and inland. Evaluation was performed on diurnal and seasonal timescales, cloud screening conditions and derived thunderstorm instability indices. At all timescales, the temperature RMSD was higher than the AIRS accuracy mission goal of ±1 K. Relative humidity RMSD was satisfactory with deviations <20% and <50% for both lower and upper troposphere respectively. AIRS retrieval of water vapour under cloudy and cloud-free conditions had no significant difference whereas cloud-free temperature was found to be more accurate. The seasonal evolution of some thunderstorm convective indices were also found to be comparable for AIRS and RAOB. The ability of AIRS to capture the evolution of these indices imply it will be a useful dataset for the African Science for Weather Information and Forecasting Techniques (SWIFT) high impact weather studies.

Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

An Assessment of the Temperature and Humidity of Atmospheric Infrared Sounder (AIRS) v6 Profiles Using Radiosonde Data in the Lee of the Tibetan Plateau

Atmospheric Infrared Sounder (AIRS) products are important for weather prediction and climate monitoring in the lee of the Tibetan Plateau (TP), where the terrain is complex. However, the quality of the AIRS products in this region remains unclear due to the unavailability of upper-air observation. In this study, for the first time, we use an 8-year intensive radiosonde observation dataset from 11 sites to assess the quality of the AIRS version 6 products in the lee of the TP at both daytime and nighttime. The results indicate that, overall, the AIRS products have a dry and cold bias in the lee of the TP, and larger biases over the sites of higher altitude. AIRS temperature retrieval has a larger deficiency in low levels at nighttime, while a better representation of moisture is found below 600 hPa over the low-altitude sites. In the lee of the TP, the quality control flags for temperature and moisture should be considered individually. The AIRS profile products could be useful for synoptic analysis of air temperature, moisture, and climate monitoring in this region, and further improvements are needed in the near-surface and nighttime retrieval processes.

Assessment of COSMIC radio occultation and AIRS hyperspectral IR sounder temperature products in the stratosphere using observed radiances

AbstractUpper air temperature is defined as an essential climate variable by the World Meteorological Organization. Two remote sensing technologies being promoted for monitoring stratospheric temperatures are GPS radio occultation (RO) and spectrally resolved IR radiances. This study assesses RO and hyperspectral IR sounder derived temperature products within the stratosphere by comparing IR spectra calculated from GPS RO and IR sounder products to coincident IR observed radiances, which are used as a reference standard. RO dry temperatures from the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission are compared to NASA Atmospheric Infrared Sounder (AIRS) retrievals using a previously developed profile‐to‐profile collocation method and vertical temperature averaging kernels. Brightness temperatures (BTs) are calculated for both COSMIC and AIRS temperature products and are then compared to coincident AIRS measurements. The COSMIC calculated minus AIRS measured BTs exceed the estimated 0.5 K measurement uncertainty for the winter time extratropics around 35 hPa. These differences are attributed to seasonal UCAR COSMIC biases. Unphysical vertical oscillations are seen in the AIRS L2 temperature product in austral winter Antarctic regions, and results imply a small AIRS tropical warm bias around ~35 hPa in the middle stratosphere.

Reanalysis of the Indian summer monsoon: four dimensional data assimilation of AIRS retrievals in a regional data assimilation and modeling framework

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003–2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis.

Improved AIRS Temperature and Moisture Soundings with Local A Priori Information for the 1DVAR Method

AbstractA moving-window regression technique was developed for obtaining better a priori information for one-dimensional variational (1DVAR) physical retrievals. Using this technique regression coefficients were obtained for a specific geographical 10° × 10° window and for a given season. Then, regionally obtained regression retrievals over East Asia were used as a priori information for physical retrievals. To assess the effect of improved a priori information on the accuracy of the physical retrievals, error statistics of the physical retrievals from clear-sky Atmospheric Infrared Sounder (AIRS) measurements during 4 months of observation (March, June, September, and December of 2010) were compared; the results obtained using new a priori information were compared with those using a priori information from a global set of training data classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the moving-window regression method can successfully improve the accuracy of physical retrieval. For temperature, root-mean-square error (RMSE) improvements of 0.1–0.2 and 0.25–0.5 K were achieved over the 150–300- and 900–1000-hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5%–3.5% above the 300-hPa level and by 0.5%–1% within the 700–950-hPa layer.

Validation of INSAT-3D temperature and moisture sounding retrievals using matched radiosonde measurements

ABSTRACTAn evaluation of temperature and moisture profiles retrieved from a geostationary Indian National Satellite (INSAT-3D) sounder, launched in 2013, is performed against collocated radiosonde (RAOB) observation measurements of more than 1 year. This evaluation is carried out in terms of bias and root mean square error (RMSE) in temperature and relative humidity. An error analysis is carried out for different surface types, different seasons and day/night cases. The key finding of this study is that INSAT-3D retrievals show good agreement with RAOB measurements with overall RMSE accuracies ~1–2 K and 10–20%, respectively, for temperature and relative humidity in the troposphere. However, the temperature and relative humidity retrievals over land or in dry atmosphere show degraded performance. This degradation might be related to uncertainty in surface emissivity over land and possibility of undetected cloud in dry atmospheric condition. In addition to it, a similar analysis is carried out to assess the relative performance of INSAT-3D-retrieved profiles, Atmospheric Infrared Sounder (AIRS) L2 Standard Physical Retrieval (AIRS-only) version 6 (AIRS2RET) profiles and European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) reanalysis with respect to spatially and temporally collocated RAOBs. In this analysis, temperature and moisture profiles from RAOBs serve as reference measurements and all retrievals and ERA-interim are compared with RAOBs. AIRS and INSAT-3D temperature retrievals gave comparable accuracies in upper and lower troposphere where as the quality degrades in middle troposphere resulting in larger errors. This may be due to improper bias correction coefficients used for brightness temperature of clear sky pixels before physical retrievals. In case of relative humidity, INSAT-3D profiles have comparable accuracies as AIRS in troposphere.

Directional gravity wave momentum fluxes in the stratosphere derived from high‐resolution AIRS temperature data

AbstractIn order to reduce uncertainties in modeling the stratospheric circulation, global observations of gravity wave momentum flux (GWMF) vectors are required for comparison with distributions of resolved and parametrized GWMF in global models. For the first time, we derive GWMF vectors globally from data of a nadir‐viewing satellite instrument: we apply a 3‐D method to an Atmospheric Infrared Sounder (AIRS) temperature data set that was optimized for gravity wave (GW) analysis. For January 2009, the resulting distributions of GW amplitudes and of net GWMF highlight the importance of GWs in the polar vortex and the summertime subtropics. Net GWMF is preferentially directed opposite to the background wind, and, interestingly, it is dominated by large‐amplitude GWs of relatively long horizontal wavelength. For convective GW sources, these large horizontal scales are in contradiction with traditional thoughts. However, the observational filter effect needs to be kept in mind when interpreting the results.

Cloud‐induced uncertainties in AIRS and ECMWF temperature and specific humidity

AbstractThe uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching −30% at 850 hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud‐cleared areas or above clouds within the field of regard. EC has small moist biases (~5–10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within ±1 K at altitudes above the 700 hPa level but increase with decreasing altitude. Cloud‐cleared retrievals lead to large AIRS cold biases (reaching about −2 K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root‐mean‐squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5–2.5 K) but slightly larger than the NNW errors for thick COD (reaching 3.5 K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.

The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011)

Atmospheric InfraRed Sounder (AIRS) measurements are a valuable supplement to current observational data, especially over the oceans where conventional data are sparse. In this study, two types of AIRS-retrieved temperature and moisture profiles, the AIRS Science Team product (SciSup) and the single field-of-view (SFOV) research product, were evaluated with European Centre for Medium-Range Weather Forecasts (ECMWF) analysis data over the Atlantic Ocean during Hurricane Ike (2008) and Hurricane Irene (2011). The evaluation results showed that both types of AIRS profiles agreed well with the ECMWF analysis, especially between 200 hPa and 700 hPa. The average standard deviation of both temperature profiles was approximately 1 K under 200 hPa, where the mean AIRS temperature profile from the AIRS SciSup retrievals was slightly colder than that from the AIRS SFOV retrievals. The mean SciSup moisture profile was slightly drier than that from the SFOV in the mid troposphere. A series of data assimilation and forecast experiments was then conducted with the Advanced Research version of the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system for hurricanes Ike and Irene. The results showed an improvement in the hurricane track due to the assimilation of AIRS clear-sky temperature profiles in the hurricane environment. In terms of total precipitable water and rainfall forecasts, the hurricane moisture environment was found to be affected by the AIRS sounding assimilation. Meanwhile, improving hurricane intensity forecasts through assimilating AIRS profiles remains a challenge for further study.

Intraseasonal temperature variability in the upper troposphere and lower stratosphere from the GPS radio occultation measurements

In this study, we examine the detailed spatiotemporal patterns and vertical structure of the intraseasonal temperature variability in the upper troposphere and lower stratosphere (UTLS) associated with the Madden‐Julian Oscillation (MJO) using the temperature profiles from the recent Global Positioning System radio occultation (GPS RO) measurements including the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. The MJO‐related temperature anomalies in the UTLS are smaller near the equator (<0.6 K) than over the subtropics and extratropics (>1.2 K). Near the equator, the temperature anomalies exhibit an eastward tilt with height from the upper troposphere (UT) to the lower stratosphere (LS) and their magnitudes and signs are determined by the strength of convective anomalies and vertical pressure level. The subtropical temperature anomalies have similar magnitudes and patterns at a given location between the UT (250 hPa to 150 hPa) and the LS (150 hPa to 50 hPa) except for opposite signs that change around 150 hPa. The subtropical warm (cold) anomalies in the UT and cold (warm) anomalies in the LS are typically collocated with the subtropical positive (negative) tropopause height anomalies/cyclones (anticyclones) and flank or lie to the west of equatorial enhanced (suppressed) convection. We also compare the intraseasonal temperature variability in the UTLS related to the MJO between the GPS RO and Atmospheric Infrared Sounder (AIRS) measurements to highlight the new features of the GPS RO temperature anomalies and to evaluate the quality of the AIRS temperature in the UTLS considering the GPS RO temperature in the UTLS as the benchmark. Both AIRS and GPS RO have a very consistent vertical structure in the subtropical UTLS with a high correlation coefficient 0.92 and similar magnitudes. Both AIRS and GPS RO also show a generally consistent vertical structure of the intraseasonal temperature anomalies in the equatorial UTLS. However, GPS RO reveals many detailed fine‐scale vertical structures of the equatorial temperature anomalies between 150 and 50 hPa that are not well captured by AIRS. Furthermore, the equatorial temperature anomalies are about 40% underestimated in AIRS in comparison to GPS RO, over the equatorial Indian and western Pacific Oceans for 250 hPa and over all longitudes for 100 hPa. The low sampling within the optically thick clouds and low vertical resolution near the tropopause may both contribute to these deficiencies of AIRS.

A Study of Dust Aerosols Impact on Hurricanes with Multi-Sensors Measurement from Space

MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Thickness (AOT) and the Atmospheric Infrared Sounder (AIRS) atmospheric profiles (temperature and moisture) Level 2 products are studied to investigate dust aerosols impacts on hurricanes through spatial analysis. Spatial regions were selected based on two concentric circles (an annulus) and segmented by left and right regions of 180° each around the hurricanes center. Statistical relationships between MODIS AOT, AIRS temperature and percent relative humidity (RH) of the hurricanes Isabel (2003), Frances (2004), Katrina (2005) and Helene (2006) are presented. The variation of temperature and RH values represented in static-left, static-right, dynamic-left and dynamic-right for the 500 mb, 700 mb and 850 mb pressure levels are analyzed to determine the impacts of dust aerosols on temperature and RH. Average AOT was found highest in Helene (0.27) than Isabel (0.23), Frances (0.18) and Katrina (0.16). Negative impacts on the atmospheric moisture levels and positive on the temperature was found by dust aerosols. This study concludes that in 2006 dust dampened the force of hurricane Helene contributing towards extremely dryer atmosphere and inhibiting the conductive to intensify the hurricane.

Assimilating AIRS Temperature and Mixing Ratio Profiles Using an Ensemble Kalman Filter Approach for Convective-Scale Forecasts

Abstract One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system

A set of data assimilation and forecast experiments is performed with the NASA Global data assimilation and forecast system GEOS‐5, to compare the impact of different approaches toward assimilation of Atmospheric Infrared Sounder (AIRS) data. The impact is first assessed globally on a sample of more than forty forecasts per experiment, through the standard 500 hPa anomaly correlation metrics. Next, the focus is on precipitation analysis and precipitation forecast skill relative to one particular event: an extreme rainfall episode which occurred in late July 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that, in addition to improving the global forecast skill, the assimilation of quality‐controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7‐day forecasts, than assimilation of clear‐sky radiances. The improvement of precipitation forecast skill up to 7 days is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low‐level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Relationship between marine boundary layer clouds and lower tropospheric stability observed by AIRS, CloudSat, and CALIOP

[1] Thirteen months of matched temperature and water vapor profiles from the Atmospheric Infrared Sounder (AIRS), collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses, National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis, and cloud profiles from the CloudSat and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments are investigated to quantify aspects of maritime boundary layer (MBL) clouds and their thermodynamic environment. This study highlights the strengths and limitations of this multisensor A-Train approach. The AIRS retrieval yield (percentage of high-quality temperature and water vapor profiles to the surface) over the oceans between 40°S and 40°N within MBL clouds is between 61% and 71% globally and is greater than 80%–90% throughout most of the subtropics. The lower tropospheric stability (LTS) and estimated inversion strength (EIS) are derived from AIRS temperature and water vapor profiles over the global oceans as well as from collocated ECMWF model analysis data. Positive values of EIS derived from AIRS well represent MBL conditions, demonstrating that AIRS contains quantitatively useful information with regard to relative changes in dynamic range of temperature and water vapor in the MBL. The relative magnitude and seasonality of LTS and EIS from the collocated satellite data set in stratocumulus regions are very similar to spatially and temporally collocated ECMWF model analyses, but differences are found between different subsampling criteria of reanalysis data. For coincident vertical profiles, the MBL in AIRS data shows a more smoothed structure than that of ECMWF. This multisensor investigation establishes a basis for using A-train observations to quantify elements of low cloud-climate feedback.

On the consistency of CERES longwave flux and AIRS temperature and humidity profiles

[1] In this paper, the temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) are evaluated with outgoing longwave radiation (OLR) from the Clouds and the Earth's Radiant Energy System (CERES) measurements. Using collocated CERES and AIRS measurements from A-train observations, the temperature and humidity profiles from the AIRS are evaluated by using them in a radiative transfer model and comparing the modeled OLR with that from the CERES. Both the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements are used to ensure a strict clear-sky condition over the CERES fields-of-view (FOVs) in the evaluation. The paper shows that model-computed OLRs using the AIRS temperature and humidity profiles and surface skin temperature agree well with CERES data for daytime oceans, indicating good accuracies of both the AIRS and the CERES products. However, it is found that a certain discrepancy exists between OLR from the modeling with the AIRS atmospheric profiles and that from the CERES measurements. For nighttime oceans, the AIRS temperature and humidity profiles and surface skin temperature likely have significant bias errors in tropical and subtropical areas that are due to undetected thin cirrus clouds. The inconsistency of the CERES and the AIRS product in OLR needs to be understood for reliable earth radiation studies.

Deep convective clouds at the tropopause

Abstract. Data from the Atmospheric Infrared Sounder (AIRS) on the EOS Aqua spacecraft each day show tens of thousands of Cold Clouds (CC) in the tropical oceans with 10 μm window channel brightness temperatures colder than 225 K. These clouds represent a mix of cold anvil clouds and Deep Convective Clouds (DCC). This mix can be separated by computing the difference between two channels, a window channel and a channel with strong CO2 absorption: for some cold clouds this difference is negative, i.e. the spectra for some cold clouds are inverted. We refer to cold clouds with spectra which are more than 2 K inverted as DCCi2. Associated with DCCi2 is a very high rain rate and a local upward displacement of the tropopause, a cold "bulge", which can be seen directly in the brightness temperatures of AIRS and Advanced Microwave Sounding Unit (AMSU) temperature sounding channels in the lower stratosphere. The very high rain rate and the local distortion of the tropopause indicate that DCCi2 objects are associated with severe storms. Significant long-term trends in the statistical properties of DCCi2 could be interesting indicators of climate change. While the analysis of the nature and physical conditions related to DCCi2 requires hyperspectral infrared and microwave data, the identification of DCCi2 requires only one good window channel and one strong CO2 sounding channel. This suggests that improved identification of severe storms with future advanced geostationary satellites could be accomplished with the addition of one or two narrow band channels.