ALBERT

Description: During several periods since 2005 the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat has performed observations dedicated to the region of the upper troposphere/lower stratosphere (UTLS). For the duration of November/December 2005 global distributions of temperature and several trace gases from MIPAS UTLS-1 mode measurements have been retrieved using the IMK/IAA (Institut für Meteorologie und Klimaforschung/Instituto de Astrofísica de Andalucía) scientific processor. In the UTLS region a vertical resolution of 3 km for temperaure, 3 to 4 km for H2O, 2.5 to 3 km for O3, 3.5 km for HNO3 and 3.5 to 2.5 km for N2O has been achieved. The retrieved temperature, H2O, O3, HNO3, N2O, and relative humidity over ice are intercompared with the Microwave Limb Sounder (MLS/Aura) v2.2 data in the pressure range 316 to 0.68 hPa, 316 to 0.68 hPa, 215 to 0.68 hPa, 215 to 3.16 hPa, 100 to 1 hPa and 316 to 10 hPa, respectively. In general, MIPAS and MLS temperatures are biased within ±4 K over the whole pressure and latitude range. Systematic, latitude-independent differences of −2 to −4 K (MIPAS-MLS) at 121 hPa are explained by previously observed biases in the MLS v2.2 temperature retrievals. Temperature differences of −4 K up to 12 K above 10.0 hPa are present both in MIPAS and MLS with respect to ECMWF (European Centre for Medium-Range Weather Forecasts) and are likely due to deficiencies of the ECMWF analysis data. MIPAS and MLS stratospheric volume mixing ratios (vmr) of H2O are biased within ±1 ppmv, with indication of oscillations between 146 and 26 hPa in the MLS dataset. Tropical upper tropospheric values of relative humidity over ice measured by the two instruments differ by ±20% in the pressure range ~146 to 68 hPa. These differences are mainly caused by the MLS temperature biases. Ozone mixing ratios agree within 0.5 ppmv (10 to 20%) between 68 and 14 hPa. At pressures smaller than 10 hPa, MIPAS O3 vmr are higher than MLS by an average of 0.5 ppmv (10%). General agreement between MIPAS and MLS HNO3 is within the range of −1.0 (−10%) to 1.0 ppbv (20%). MIPAS HNO3 is 1.0 ppbv (10%) higher compared to MLS between 46 hPa and 10 hPa over the Northern Hemisphere. Over the tropics at 31.6 hPa MLS shows a low bias of more than 1 ppbv (〉50%). In general, MIPAS and MLS N2O vmr agree within 20 to 40 ppbv (20 to 40%). Differences in the range between 100 to 21 hPa are attributed to a known 20% positive bias in MIPAS N2O data.

Description: The Measurements of Humidity in the Atmosphere and Validation Experiment (MOHAVE) 2009 campaign took place on 11–27 October 2009 at the JPL Table Mountain Facility in California (TMF). The main objectives of the campaign were to (1) validate the water vapor measurements of several instruments, including, three Raman lidars, two microwave radiometers, two Fourier-Transform spectrometers, and two GPS receivers (column water), (2) cover water vapor measurements from the ground to the mesopause without gaps, and (3) study upper tropospheric humidity variability at timescales varying from a few minutes to several days. A total of 58 radiosondes and 20 Frost-Point hygrometer sondes were launched. Two types of radiosondes were used during the campaign. Non negligible differences in the readings between the two radiosonde types used (Vaisala RS92 and InterMet iMet-1) made a small, but measurable impact on the derivation of water vapor mixing ratio by the Frost-Point hygrometers. As observed in previous campaigns, the RS92 humidity measurements remained within 5% of the Frost-point in the lower and mid-troposphere, but were too dry in the upper troposphere. Over 270 h of water vapor measurements from three Raman lidars (JPL and GSFC) were compared to RS92, CFH, and NOAA-FPH. The JPL lidar profiles reached 20 km when integrated all night, and 15 km when integrated for 1 h. Excellent agreement between this lidar and the frost-point hygrometers was found throughout the measurement range, with only a 3% (0.3 ppmv) mean wet bias for the lidar in the upper troposphere and lower stratosphere (UTLS). The other two lidars provided satisfactory results in the lower and mid-troposphere (2–5% wet bias over the range 3–10 km), but suffered from contamination by fluorescence (wet bias ranging from 5 to 50% between 10 km and 15 km), preventing their use as an independent measurement in the UTLS. The comparison between all available stratospheric sounders allowed to identify only the largest biases, in particular a 10% dry bias of the Water Vapor Millimeter-wave Spectrometer compared to the Aura-Microwave Limb Sounder. No other large, or at least statistically significant, biases could be observed. Total Precipitable Water (TPW) measurements from six different co-located instruments were available. Several retrieval groups provided their own TPW retrievals, resulting in the comparison of 10 different datasets. Agreement within 7% (0.7 mm) was found between all datasets. Such good agreement illustrates the maturity of these measurements and raises confidence levels for their use as an alternate or complementary source of calibration for the Raman lidars. Tropospheric and stratospheric ozone and temperature measurements were also available during the campaign. The water vapor and ozone lidar measurements, together with the advected potential vorticity results from the high-resolution transport model MIMOSA, allowed the identification and study of a deep stratospheric intrusion over TMF. These observations demonstrated the lidar strong potential for future long-term monitoring of water vapor in the UTLS.

Description: Sulfur dioxide (SO2) is an important atmospheric constituent, particularly in the aftermath of volcanic eruptions. These events can inject large amounts of SO2 into the lower stratosphere, where it is oxidised to form sulfate aerosols; these in turn have a significant effect on the climate. The MLS instrument on the Aura satellite has observed the SO2 mixing ratio in the upper troposphere and lower stratosphere from August 2004 to the present, during which time a number of volcanic eruptions have significantly affected those regions of the atmosphere. We describe the MLS SO2 data and how various volcanic events appear in the data. As the MLS SO2 data are currently not validated we take some initial steps towards their validation. First we establish the level of internal consistency between the three spectral regions in which MLS is sensitive to SO2. We compare SO2 column values calculated from MLS data to total column values reported by the OMI instrument. The agreement is good (within about 1 DU) in cases where the SO2 is clearly at altitudes above 147 hPa.

Description: Now that the Earth has been monitored by satellites for more than 40 years, Earth observation images can be used to study how the Earth system behaves over extended periods. Such long-term studies require the combination of data from multiple instruments, with the earliest datasets being of particular importance in establishing a baseline for trend analysis. As the quality of these earlier datasets is often lower, careful quality control is essential, but the sheer size of these image sets makes an inspection by hand impracticable. Therefore, one needs to resort to automatic methods to inspect these Earth observation images for anomalies. In this paper, we describe the design of a system that performs an automatic anomaly analysis on Earth observation images, in particular the Meteosat First Generation measurements. The design of this system is based on a preliminary analysis of the typical anomalies that can be found in the dataset. This preliminary analysis was conducted by hand on a representative subset and resulted in a finite list of anomalies that needed to be detected in the whole dataset. The automated anomaly detection system employs a dedicated detection algorithm for each of these anomalies. The result is a system with a high probability of detection and low false alarm rate. Furthermore, most of these algorithms are able to pinpoint the anomalies to the specific pixels affected in the image, allowing the maximum use of the data available.

Description: High-accuracy continuous measurements of greenhouse gases (CO2 and CH4) during the BARCA (Balanço Atmosférico Regional de Carbono na Amazônia) phase B campaign in Brazil in May 2009 were accomplished using a newly available analyzer based on the cavity ring-down spectroscopy (CRDS) technique. This analyzer was flown without a drying system or any in-flight calibration gases. Water vapor corrections associated with dilution and pressure-broadening effects for CO2 and CH4 were derived from laboratory experiments employing measurements of water vapor by the CRDS analyzer. Before the campaign, the stability of the analyzer was assessed by laboratory tests under simulated flight conditions. During the campaign, a comparison of CO2 measurements between the CRDS analyzer and a nondispersive infrared (NDIR) analyzer on board the same aircraft showed a mean difference of 0.22±0.09 ppm for all flights over the Amazon rain forest. At the end of the campaign, CO2 concentrations of the synthetic calibration gases used by the NDIR analyzer were determined by the CRDS analyzer. After correcting for the isotope and the pressure-broadening effects that resulted from changes of the composition of synthetic vs. ambient air, and applying those concentrations as calibrated values of the calibration gases to reprocess the CO2 measurements made by the NDIR, the mean difference between the CRDS and the NDIR during BARCA was reduced to 0.05±0.09 ppm, with the mean standard deviation of 0.23±0.05 ppm. The results clearly show that the CRDS is sufficiently stable to be used in flight without drying the air or calibrating in flight and the water corrections are fully adequate for high-accuracy continuous airborne measurements of CO2 and CH4.

Description: An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example. An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M) of pseudochannels while also regularizing the background (geophysical plus instrument) noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance. The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals – whether Bayesian or not – lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.

Description: We present descriptions of the in situ instrumentation, calibration procedures, intercomparison efforts, and data filtering methods used in a 39-yr record of continuous atmospheric carbon dioxide (CO2) observations made at Baring Head, New Zealand. Located on the southern coast of the North Island, Baring Head is exposed to extended periods of strong air flow from the south with minimal terrestrial influence resulting in low CO2 variability. The site is therefore well suited for sampling air masses that are representative of the Southern Ocean region. Instrumental precision is better than 0.015 ppm (1-σ) on 1-Hz values. Comparisons to over 600 co-located flask samples, as well as laboratory based flask and cylinder comparison exercises, suggest that over recent decades compatibility with respect to the Scripps Institution of Oceanography (SIO) and World Meteorological Organisation (WMO) CO2 scales has been 0.3 ppm or better.

Description: An automated Fourier Transform Spectroscopic (FTS) solar observatory was established in Darwin, Australia in August 2005. The laboratory is part of the Total Carbon Column Observing Network, and measures atmospheric column abundances of CO2 and O2 and other gases. Measured CO2 columns were calibrated against integrated aircraft profiles obtained during the TWP-ICE campaign in January–February 2006, and show good agreement with calibrations for a similar instrument in Park Falls, Wisconsin. A clear-sky low airmass relative precision of 0.1% is demonstrated in the CO2 and O2 retrieved column-averaged volume mixing ratios. The 1% negative bias in the FTS XCO2 relative to the World Meteorological Organization (WMO) calibrated in situ scale is within the uncertainties of the NIR spectroscopy and analysis.

Description: Clouds and associated precipitation are the largest source of uncertainty in current weather and future climate simulations. Observations of the microphysical, dynamical and radiative processes that act at cloud scales are needed to improve our understanding of clouds. The rapid expansion of ground-based super-sites and the availability of continuous profiling and scanning multi-frequency radar observations at 35 and 94 GHz have significantly improved our ability to probe the internal structure of clouds in high temporal-spatial resolution, and to retrieve quantitative cloud and precipitation properties. However, there are still gaps in our ability to probe clouds due to large uncertainties in the retrievals. The present work discusses the potential of G band (frequency between 110 and 300 GHz) Doppler radars in combination with lower frequencies to further improve the retrievals of microphysical properties. Our results show that, thanks to a larger dynamic range in dual-wavelength reflectivity, dual-wavelength attenuation and dual-wavelength Doppler velocity (with respect to a Rayleigh reference), the inclusion of frequencies in the G band can significantly improve current profiling capabilities in three key areas: boundary layer clouds, cirrus and mid-level ice clouds, and precipitating snow.

Description: Methane is the second most important greenhouse gas after CO2 and contributes to global warming. Its sources are not uniformly distributed across terrestrial and aquatic ecosystems, and most of the methane flux is expected to stem from hotspots which often occupy a very small fraction of the total landscape area. Continuous time-series measurements of CH4 concentrations can help identify and locate these methane hotspots. Newer, low-cost trace gas sensors such as the Figaro TGS 2600 can detect CH4 even at ambient concentrations. Hence, in this paper we tested this sensor under real-world conditions over Toolik Lake, Alaska, to determine its suitability for preliminary studies before placing more expensive and service-intensive equipment at a given locality. A reasonably good agreement with parallel measurements made using a Los Gatos Research FMA 100 methane analyzer was found after removal of the strong sensitivities for temperature and relative humidity. Correcting for this sensitivity increased the absolute accuracy required for in-depth studies, and the reproducibility between two TGS 2600 sensors run in parallel is very good. We conclude that the relative CH4 concentrations derived from such sensors are sufficient for preliminary investigations in the search of potential methane hotspots.