Using Python language for analysing measurements from SABER instrument on TIMED satellite

The practical handling and analysis of satellite data is outlined using the programming language Python. The limb sounding technique of the SABER instrument on board of the TIMED satellite delivers vertical profiles of kinematic temperature from the stratosphere (∼30 km) up to the lower thermosphere (∼120 km). The procedure may be summarised as follow: In the first step the level 2 data for one month are extracted from the netCDF format and arranged into a new altitude-latitude grid for the ascending and descending orbits. The longitudinal structure is rearranged applying the decomposition into zonal harmonics. Various cross sections of the data give a good overview of the thermal structure and dynamics of the atmosphere up to 120 km. The monthly values of the zonal averaged temperature are compared to the available data from stratospheric reanalyses up to 60 km as well as the initialized background climatology of general circulation models for the middle atmosphere.In diesem Artikel soll der praktische Umgang mit Satellitendaten und deren Auswertung unter Verwendung der Programmiersprache Python skizziert werden. Auf der Basis der Horizontsondierungen des SABER Instruments auf dem TIMED Satelliten werden vertikale Profile wie die kinetischen Temperatur von der Stratosphäre (∼30 km) bis zur unteren Thermosphäre (∼120 km) gewonnen. Die Arbeitsschritte bei der Analyse lassen sich wie folgt gliedern: Als erstes werden die Level 2 Produkte eines Monats aus dem netCDF Format extrahiert und an ein neues Höhen-Breiten Gitter für jeden auf- und absteigenden Orbit angepasst. Die Längenstruktur wird mit Hilfe einer Zerlegung in harmonische Funktionen regularisiert. Diverse Querschnitte der Daten geben ein guten Überblick über die thermischen Struktur und Dynamik der Atmosphäre bis 120 km. Die Monatswerte des Zonalmittels der Temperatur werden mit denen aus operationellen Reanalysedaten (∼60 km) sowie der Hintergrundklimatologie von Zirkulationsmodellen der mittleren Atmosphäre verglichen

Atmospheric methane with SCIAMACHY: Operational Level 2 data analysis and verification

SCIAMACHY is a passive imaging spectrometer mounted on board ESA’s ENVISAT satellite to probe a large number of atmospheric trace gas species, such as methane, and their global distribution and evolution. Methane (CH4) is particularly interesting as it is one of the most abundant greenhouse gas in the Earth atmosphere. To analyze SCIAMACHY methane measurements, we used the DLR BIRRA (Beer InfraRed Retrieval Algorithm) to retrieve nadir methane concentrations from its infrared spectra in channel 6. By integrating the DLR BIRRA code into ESAs operational Level 2 processor, we expanded it to include atmospheric CH4 column measurements. We have therefore performed an extensive test and verification operation. Our tests are based on separate comparisons with existing space and ground-based obtained measurements of methane column density. We present here our strategy for quality check of this first version of a CH4 product. We will further discuss specific geographical areas we used to validate the products

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Intercomparison of Near Infrared SCIAMACHY and Thermal Infrared Nadir Vertical Column Densities

Nadir infrared (IR) sounding can be used to derive information on trace gases relevant for climate and air quality. For vertical column density retrievals using SCIAMACHY near IR nadir observations, the BIRRA (Beer InfraRed Retrieval Algorithm) code has recently been implemented in the operational level 1 – 2 processor. For analysis of thermal IR nadir observations of AIRS, GOSAT, IASI, or TES, a closely related code CERVISA (Column EstimatoR Vertical Infrared Sounding of the Atmosphere) has been developed. Both codes share a large portion of modules, e.g., for line-by-line absorption and the nonlinear least squares solver. The essential difference is the part of the forward model devoted to radiative transfer through the atmosphere, i.e., Beer’s law for the near IR versus Schwarzschild’s equation for the thermal IR. For the ongoing validation of the BIRRA carbon monoxide CO and methane CH4 products intercomparisons with thermal IR sounding data are performed. CERVISA retrieval results are compared both to the operational products of the IR sounder considered and to SCIAMACHY products retrieved with BIRRA

Py4CAtS - Python Tools for Line-by-Line Modelling of Infrared Atmospheric Radiative Transfer

Py4CAtS — Python scripts for Computational ATmospheric Spectroscopy is a Python re-implementation of the Fortran infrared radiative transfer code GARLIC, where compute-intensive code sections utilize the Numeric/Scientific Python modules for highly optimized array-processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented

Retrieval of CO Vertical Columns from SCIAMACHY Infrared Nadir Observations

Nadir observations in the shortwave infrared channels of SCIAMACHY onboard the ENVISAT satellite can be used to derive infor- mation on CO, CH4, N2O, CO2, and H2O. BIRRA (Beer InfraRed Retrieval Algorithm) is a least squares fit of the measured radiance with respect to molecular column densities and auxiliary parameters, nb. surface albedo, baseline and slit function width. Here special features of the code are shown along with results of carbon monoxide retrievals from SCIAMACHY near infrared nadir observations. In particular intercomparisons with other SCIAMACHY Infrared Nadir retrievals and with AIRS (Atmospheric Infrared Sounder onboard NASA-EOS-Aqua) will be presented

Retrieval of Carbon Monoxide Vertical Column Densities from SCIAMACHY Infrared Nadir Observations

Nadir observations in the shortwave infrared channels of SCIAMACHY onboard the ENVISAT satellite can be used to derive information on CO, CH4, N2O, CO2, and H2O. BIRRA (Beer InfraRed Retrieval Algorithm) is a least squares fit of the measured radiance with respect to molecular column densities and auxiliary parameters, nb. surface albedo, baseline and slit function width. Here special features of the code are shown along with results of carbon monoxide retrievals from SCIAMACHY near infrared nadir observations. In particular intercomparisons with other SCIAMACHY Infrared Nadir retrievals and with AIRS (Atmospheric Infrared Sounder onboard NASA-EOS-Aqua) will be presented

A new algorithm for the downscaling of cloud fields

We present a novel algorithm for the downscaling of three-dimensional cloud fields. The goal of the algorithm is to add realistic subscale variability to a coarse field taking the resolved variability into account. The method is tested by coarse graining high-resolution sparse cumulus and broken stratocumulus clouds in the horizontal plane, downscaling these coarse fields back to the high resolution and comparing the radiative and microphysical properties of these downscaled fields with the original high-resolution fields. The resolutions of the cumulus and stratocumulus clouds used for this purpose are increased by a factor of four and ten, respectively. The downscaling decreases the errors in the flux transmittance and reflectance of the cumulus and stratocumulus cloud fields by at least a factor of ten and three, respectively, compared to utilising the coarse cloud fields. A novel aspect of our algorithm is the fact that it constrains the high-resolution fields of cloud liquid water content as well as the subscale cloud fraction. An alternative version that does not include cloud fraction information is less accurate, but still significantly better than using the coarse fields. The latter downscaling algorithm can also be utilised for the disaggregation of geophysical fields for which fractional coverages are not defined. Furthermore, the downscaling algorithm can be combined with our other algorithms to generate surrogate fields with other constraints, for example, surrogate clouds with a prescribed liquid water content height distribution

Py4CAtS - PYthon for Computational ATmospheric Spectroscopy

Line-by-line models have become mandatory for many atmospheric spectroscopy applications. A large variety of models has been developed in the past, but in general these codes work as a kind of 'black box' where intermediate quantities such as cross sections are difficult to access. Py4CAtS --- PYthon scripts for Computational ATmospheric Spectroscopy is a Python re-implementation of our Fortran infrared radiative transfer code GARLIC (Generic Atmospheric Radiation Line-by-line Infrared Code), where compute-intensive code sections utilize the Numeric/Scientific Python modules for highly optimized array-processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. Py4CAtS can be used in two ways, from the Unix/Linux (or Windows/Mac) console/terminal or inside the (i)python interpreter. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented

First Results of Atmospheric Composition Retrieval using IASI-METOP and AIRS-AQUA Data

Regularization methods for the inversion of infrared nadir sounding observations are currently investigated. An iterative Runge-Kutta type method for nonlinear ill-posed problems has been implemented and its performance has been studied using synthetic measurements. Comparisons with Tikhonov type inversion with a priori regularization parameter selection indicate that both methods are of similar accuracy; however, the Runge-Kutta method is less sensitive to regularization parameter variations. Furthermore, vertical column density retrieval from nadir infrared sounders such as AIRS will be used for validation of column densities retrieved from near infrared SCIAMACHY observations. Two closely related retrieval codes are used for L2 processing of SCIAMACHY near infrared and AIRS mid infrared spectra. First results of this intercomparison are shown