Observation Impact over the Antarctic During the Concordiasi Field Campaign

The impact of observations on analysis uncertainty and forecast performance was investigated for Austral Spring 2010 over the Southern polar area for four different systems (NRL, GMAO, ECMWF and Meteo-France), at the time of the Concordiasi field experiment. The largest multi model variance in 500 hPa height analyses is found in the southern sub-Antarctic oceanic region, where there are strong atmospheric dynamics, rapid forecast error growth, and fewer upper air wind observation data to constrain the analyses. In terms of data impact the most important observation components are shown to be AMSU, IASI, AIRS, GPS-RO, radiosonde, surface and atmospheric motion vector observations. For sounding data, radiosondes and dropsondes, one can note a large impact of temperature at low levels and a large impact of wind at high levels. Observing system experiments using the Concordiasi dropsondes show a large impact of the observations over the Antarctic plateau extending to lower latitudes with the forecast range, with a large impact around 50 to 70deg South. These experiments indicate there is a potential benefit of better using radiance data over land and sea-ice and innovative atmospheric motion vectors obtained from a combination of various satellites to fill the current data gaps and improve NWP in this region

Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system

The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems. The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties

Supercooled liquid water cloud observed, analysed, and modelled at the top of the planetary boundary layer above Dome C, Antarctica

Abstract. A comprehensive analysis of the water budget over the Dome C (Concordia, Antarctica) station has been performed during the austral summer 2018–2019 as part of the Year of Polar Prediction (YOPP) international campaign. Thin (∼100 m deep) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization lidar, the H2O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), net surface radiation from the Baseline Surface Radiation Network (BSRN)), radiosondes, and satellite observations (CALIOP, Cloud-Aerosol LIdar with Orthogonal Polarization/CALIPSO, Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) combined with a specific configuration of the numerical weather prediction model: ARPEGE-SH (Action de Recherche Petite Echelle Grande Echelle – Southern Hemisphere). The analysis shows that SLW clouds were present from November to March, with the greatest frequency occurring in December and January when ∼50 % of the days in summer time exhibited SLW clouds for at least 1 h. Two case studies are used to illustrate this phenomenon. On 24 December 2018, the atmospheric planetary boundary layer (PBL) evolved following a typical diurnal variation, which is to say with a warm and dry mixing layer at local noon thicker than the cold and dry stable layer at local midnight. Our study showed that the SLW clouds were observed at Dome C within the entrainment and the capping inversion zones at the top of the PBL. ARPEGE-SH was not able to correctly estimate the ratio between liquid and solid water inside the clouds with the liquid water path (LWP) strongly underestimated by a factor of 1000 compared to observations. The lack of simulated SLW in the model impacted the net surface radiation that was 20–30 W m−2 higher in the BSRN observations than in the ARPEGE-SH calculations, mainly attributable to the BSRN longwave downward surface radiation being 50 W m−2 greater than that of ARPEGE-SH. The second case study took place on 20 December 2018, when a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top. SLW cloud appearance within the entrainment and capping inversion zones coincided with the warm and wet event. The amount of liquid water measured by HAMSTRAD was ∼20 times greater in this perturbed PBL than in the typical PBL. Since ARPEGE-SH was not able to accurately reproduce these SLW clouds, the discrepancy between the observed and calculated net surface radiation was even greater than in the typical PBL case, reaching +50 W m−2, mainly attributable to the downwelling longwave surface radiation from BSRN being 100 W m−2 greater than that of ARPEGE-SH. The model was then run with a new partition function favouring liquid water for temperatures below −20 down to −40 ∘C. In this test mode, ARPEGE-SH has been able to generate SLW clouds with modelled LWP and net surface radiation consistent with observations during the typical case, whereas, during the perturbed case, the modelled LWP was 10 times less than the observations and the modelled net surface radiation remained lower than the observations by ∼50 W m−2. Accurately modelling the presence of SLW clouds appears crucial to correctly simulate the surface energy budget over the Antarctic Plateau

Multi-frequency seismic study of the gas hydrate accumulations in Lake Baikal, Siberia

Recently, the presence of methane hydrates has been evidenced in Lake Baikal, Siberia, by means of seismic profiling, deep drilling and shallow coring. This is -up to now- the only reported occurrence of gas hydrates in a confined fresh-water basin. In this presentation, we discuss the frequency-dependent acoustic characteristics of the hydrate-bearing sediments, using 5 different types of reflection seismic data encompassing frequencies from 10 to 1000 Hz. On low-frequency airgun-array data, the base of the hydrate stability zone (HSZ) is observed as a single, high-amplitude, inverse-polarity reflection that often crosscuts the local stratigraphy. Amplitude and continuity of the BSR decrease or even disappear on higher-frequency data. On medium- to high-frequency data (e.g. watergun) the base of the HSZ is no longer expressed as a single reflector, but rather as a facies change between enhanced reflections below and blanked reflections above. The increasing reflection amplitude of the BSR with increasing offset (AVO-analysis), the high reflection coefficient of the BSR (-40 % of lake floor reflection) and the presence of enhanced reflections beneath the BSR suggest the presence of free gas below the HSZ. The observation of some enhanced reflections extending into the HSZ could even indicate that free gas may co-exist with hydrates within the HSZ. Blanking of the reflection amplitudes above the BSR is variable. Instantaneous frequency analyses reveal a low-frequency shadow beneath the BSR. We also collected lake-bottom reflection/refraction data, using GEOMAR's "Ocean-Bottom Hydrophones". Several profiles were recorded with a medium-resolution single airgun with sufficient energy to penetrate below the HSZ. The velocity information obtained from these measurements shows a distinct low-velocity layer below the base of the HSZ. Above, several higher-velocity layers are recognised. Modelling of interval velocities in this zone indicate hydrate presence of 5 to 8 % of pore volume. We also acquired new medium-frequency, single-channel airgun data at the BDP-1997 site (Baikal Drilling Project), providing the first acoustic images from this location. Hydrates (10 % pore volume) were retrieved from 121 and 160 m sub-bottom depth, but still about 200 m above the base of the local hydrate stability field. Remarkably, the seismic data at the drilling site show no indications for the presence of hydrates at the hydrate-recovery depths (no acoustic blanking, no BSR). These results were used to roughly estimate the amount of carbon stored in the Lake Baikal hydrate reservoirs, showing that most probably they do not form a future energy resource

Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables

Besides their strong contribution to weather forecast improvement through data assimilation, thermal infrared sounders onboard polar-orbiting platforms are now playing a key role for monitoring atmospheric composition changes. The Infrared Atmospheric Sounding Interferometer (IASI) instrument developed by the French space agency (CNES) and launched by Eumetsat onboard the Metop satellite series is providing essential inputs for weather forecasting and pollution/climate monitoring owing to its smart combination of large horizontal swath, good spectral resolution and high radiometric performance. EUMETSAT is currently preparing the next polar-orbiting program (EPS-SG) with the Metop-SG satellite series that should be launched around 2020. In this framework, CNES is studying the concept of a new instrument, the IASI-New Generation (IASI-NG), characterized by an improvement of both spectral and radiometric characteristics as compared to IASI, with three objectives: (i) continuity of the IASI/Metop series; (ii) improvement of vertical resolution; (iii) improvement of the accuracy and detection threshold for atmospheric and surface components. In this paper, we show that an improvement of spectral resolution and radiometric noise fulfill these objectives by leading to (i) a better vertical coverage in the lower part of the troposphere, thanks to the increase in spectral resolution; (ii) an increase in the accuracy of the retrieval of several thermodynamic, climate and chemistry variables, thanks to the improved signal-to-noise ratio as well as less interferences between the signatures of the absorbing species in the measured radiances. The detection limit of several atmospheric species is also improved. We conclude that IASI-NG has the potential for strongly benefiting the numerical weather prediction, chemistry and climate communities now connected through the European GMES/Copernicus initiative

Evaluation of Radiative Transfer Models With Clouds

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm^(−1) have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm^(−1) at night are reasonably consistent with results at 900 cm^(−1). Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm^(−1) are inferior to those at 900 cm^(−1) for daytime calculations