Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

We use microwave retrievals of upper tropospheric humidity (UTH) to estimate the impact of clear-sky-only sampling by infrared instruments on the distribution, variability and trends in UTH. Our method isolates the impact of the clear-sky-only sampling, without convolving errors from other sources. On daily time scales IR-sampled UTH contains large data gaps in convectively active areas, with only about 20-30 % of the tropics (30 S­ 30 N) being sampled. This results in a dry bias of about -9 %RH in the area-weighted tropical daily UTH time series. On monthly scales, maximum clear-sky bias (CSB) is up to -30 %RH over convectively active areas. The magnitude of CSB shows significant correlations with UTH itself (-0.5) and also with the variability in UTH (-0.6). We also show that IR-sampled UTH time series have higher interannual variability and smaller trends compared to microwave sampling. We argue that a significant part of the smaller trend results from the contrasting influence of diurnal drift in the satellite measurements on the wet and dry regions of the tropics

Error correlations in High-Resolution Infrared Radiation Sounder (HIRS) Radiances

The High-resolution Infrared Radiation Sounder (HIRS) has been flown on 17 polar-orbiting satellites between the late 1970s and the present day. HIRS applications require accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count deviations for sequential sets of calibration measurements, and then correlating deviations between channels (for a fixed view) or views (for a fixed channel). The inter-channel error covariance is usually assumed to be diagonal, but we show that large error correlations, both positive and negative, exist between channels and between views close in time. We show that correlated error exists for all HIRS and that the degree of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5 are not unusual. Correlations between calibration observations taken sequentially in time arise from periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that, for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies. These findings are significant for application of HIRS data in various applications, and related information will be made available as part of an upcoming Fundamental Climate Data Record covering all HIRS channels and satellites

Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs)

Simultaneous all angle collocations (SAACs) of microwave humidity sounders (AMSU-B and MHS) on-board polar orbiting satellites are used to estimate scan-dependent biases. This method has distinct advantages over previous methods, such as that the estimated scan-dependent biases are not influenced by diurnal differences between the edges of the scan and the biases can be estimated for both sides of the scan. We find the results are robust in the sense that biases estimated for one satellite pair can be reproduced by double differencing biases of these satellites with a third satellite. Channel 1 of these instruments shows the least bias for all satellites. Channel 2 has biases greater than 5 K, thus needs to be corrected. Channel 3 has biases of about 2 K and more and they are time varying for some of the satellites. Channel 4 has the largest bias which is about 15 K when the data are averaged for 5 years, but biases of individual months can be as large as 30 K. Channel 5 also has large and time varying biases for two of the AMSU-Bs. NOAA-15 (N15) channels are found to be affected the most, mainly due to radio frequency interference (RFI) from onboard data transmitters. Channel 4 of N15 shows the largest and time varying biases, so data of this channel should only be used with caution for climate applications. The two MHS instruments show the best agreement for all channels. Our estimates may be used to correct for scan-dependent biases of these instruments, or at least used as a guideline for excluding channels with large scan asymmetries from scientific analyses

Simulating the effects of mid- to upper-tropospheric clouds on microwave emissions in EC-Earth using COSP

In this work, the Cloud Feedback Model Intercomparison (CFMIP) Observation Simulation Package (COSP) is expanded to include scattering and emission effects of clouds and precipitation at passive microwave frequencies. This represents an advancement over the official version of COSP (version 1.4.0) in which only clear-sky brightness temperatures are simulated. To highlight the potential utility of this new microwave simulator, COSP results generated using the climate model EC-Earth's version 3 atmosphere as input are compared with Microwave Humidity Sounder (MHS) channel (190.311 GHz) observations. Specifically, simulated seasonal brightness temperatures (TB) are contrasted with MHS observations for the period December 2005 to November 2006 to identify possible biases in EC-Earth's cloud and atmosphere fields. The EC-Earth's atmosphere closely reproduces the microwave signature of many of the major large-scale and regional scale features of the atmosphere and surface. Moreover, greater than 60 % of the simulated TB are within 3 K of the NOAA-18 observations. However, COSP is unable to simulate sufficiently low TB in areas of frequent deep convection. Within the Tropics, the model's atmosphere can yield an underestimation of TB by nearly 30 K for cloudy areas in the ITCZ. Possible reasons for this discrepancy include both incorrect amount of cloud ice water in the model simulations and incorrect ice particle scattering assumptions used in the COSP microwave forward model. These multiple sources of error highlight the non-unique nature of the simulated satellite measurements, a problem exacerbated by the fact that EC-Earth lacks detailed micro-physical parameters necessary for accurate forward model calculations. Such issues limit the robustness of our evaluation and suggest a general note of caution when making COSP-satellite observation evaluations

Radiance uncertainty characterisation to facilitate climate data record creation

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented

Systematic and random errors between collocated satellite ice water path observations

There remains large disagreement between ice-water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (!30 " latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres-Extended (PATMOS-x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m -2 ). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m -2 . The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS-DARDAR, but the systematic bias is nearly an order of magnitude

Methane cross-validation between three Fourier Transform Spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three datasets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier Transform Spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier Transform Infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Lab at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1° of freedom per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS–PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ± 3 × 1022 molecules cm−2. Expressed as median ± median absolute deviation (expressed in absolute or relative terms), these differences are 0.11 ± 9.60 × 10^20 molecules cm−2 (0.012 ± 1.018 %) for TANSO-FTS–PEARL-FTS, −2.6 ± 2.6 × 10^21 molecules cm−2 (−1.6 ± 1.6 %) for ACE-FTS–PEARL-FTS, and 7.4 ± 6.0 × 10^20 molecules cm−2 (0.78 ± 0.64 %) for TANSO-FTS–ACE-FTS. The differences for ACE-FTS–PEARL-FTS and TANSO-FTS–PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS–ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns

Microwave and infrared remote sensing of ice clouds : measurements and radiative transfer simulations

This licentiate thesis considers the combination of multiple instruments for remote sensing of the Earth atmosphere from space. The primary focus is on remote sensing of atmospheric ice. Ice clouds are important for the Earth’s radiation budget, but their properties are difficult to measure and therefore poorly known. A better quantification of ice clouds is needed to improve global climate models. This thesis introduces the reader to the subject and describes how to combine measurements and radiative transfer simulations in an attempt to improve our understanding. A major part of this work is the development of a toolkit to find co-incident measurements, or collocations, between any pair of down-looking satellite sensors. Firstly, this toolkit is used to collocate passive microwave and thermal infrared sensors on meteorological satellites with the Cloud Profiling Radar on CloudSat. With the resulting collocated dataset, the Ice Water Path (IWP) signal in passive thermal radiation is studied and an improved IWP retrieval is presented. The toolkit is also used to better characterise the bias between different copies of passive microwave radiometers on-board polar-orbiting operational satellites. For the Atmospheric Radiative Transfer Simulator (ARTS), version 2, an optimised frequency grid for infrared broadband simulations is shown to be applicable for cloudy simulations. This frequency grid can and will be used to study the IWP signal in thermal infrared radiances. An outlook on a comparison between collocations and simulations is presented in the thesis

Collocating satellite-based radar and radiometer measurements to develop an ice water path retrieval

Remote sensing satellites can roughly be divided in operational satellites and scientific satellites. Generally speaking, operational satellites have a long lifetime and often several near-identical copies, whereas scientific satellites are unique and have a more limited lifetime, but produce more advanced data. An example of a scientific satellite is the CloudSat, a NASA satellite flying in the so-called "A-Train" formation with other satellites. Examples of operational satellites are the NOAA and MetOp meteorological satellite series. CloudSat carries a 94 GHz nadir viewing radar instrument measuring profiles of clouds. The NOAA-15 to NOAA-18 and MetOp-A satellites carry radiometers at various frequencies ranging from the infrared (3.76 micrometer) to around 183 GHz (approximately 1.6 mm). The full range is covered by the High Resolution Infrared Radiation Sounder (HIRS) and the Advanced Microwave Sounding Units (AMSU-A and AMSU-B). On newer satellites, AMSU-B has been replaced by the Microwave Humidity Sounder (MHS) with nearly the same characteristics. Those instruments scan the atmosphere at angles from approximately -50 to +50 degrees perpendicular to the ground track. The large amount of data from operational satellites is interesting to the scientific community, particularly when combined with measurements from a scientific satellite. The degree project focuses on this combination and consists of two parts: * The first part of the project involves searching for collocations between the CloudSat radar and one of the NOAA or MetOp-A instruments. A collocation between two instruments is defined to occur when both look at the same place at the same time (within pre-set thresholds). This has been done with software developed by the student. * Those collocations are then used to find the relation between the radiances and physical data (such as Ice Water Path (IWP)) derived from CloudSat measurements. For the tropical ocean, this relation has been compared with data from models. Additionally, an artificial neural network has been trained to retrieve IWP.Validerat; 20101217 (root

Remote sensing of ice clouds : synergistic measurements and radiative transfer simulations

This thesis primarily considers the spaceborne remote sensing of ice clouds and frozen precipitation. Ice clouds are important for hydrology and for the Earth’s radiation budget, but many properties are difficult to measure, in particular using spaceborne instruments. A better quantification of ice clouds is needed to improve global climate models. This thesis presents steps toward such an improvement.The first part of the thesis introduces topics related to the research presented in the second part, but presents no new scientific results. It gives a brief introduction to the history of atmospheric remote sensing and describes how the different parts of the electromagnetic spectrum can be used actively or passively. Then, it describes why ice clouds are important and what microphysical, optical, and macrophysical properties are used to describe atmospheric ice. Next, it briefly introduces the relevant topics in atmospheric radiative transfer. The first part concludes with a description of various approaches to retrievals, with a particular focus on those applied in this thesis.The second part of the thesis describes new results. The bulk of the new results is described in five peer-reviewed publications, that are appended verbatim.A major part of the work builds on the development of a toolkit to easily find co-incident measurements, or collocations, between any pair of satellite sensors. Four appended articles rely on this toolkit.The first appended article uses the toolkit to obtain collocations between passive microwave and infrared on operational meteorological satellites with the Cloud Profiling Radar on CloudSat. It presents three examples. Firstly, from the collocated dataset and a dataset of synthetic profiles, the article compares the statistical relations between an official CloudSat Ice Water Path (IWP) product and microwave radiances. Secondly, it shows a point-by-point comparison between the same CloudSat IWP product, and an IWP product based on passive microwave. A more sophisticated set of systematic comparisons, including more satellites and sensors, is presented in a dedicated paper. Finally, the first paper provides a first preview of how the collocations can be used to train a new IWP retrieval from passive operational measurements. This too is the topic of a dedicated paper, where solar, terrestrial infrared, and microwave radiances are combined to obtain an improved IWP product from passive operational sensors, by training with an active combined radar-lidar product from CloudSat-CALIPSO.The second appended article also relies on the collocations toolkit. Here, collocations between different copies of identical or very similar microwave sounders are used to assess how the inter-satellite bias depends on radiance and latitude.The remaining two studies described in the thesis do not use existing measurements, but are based on radiative transfer modelling. One attached paper verifies that optimised frequency grids obtained in clear-sky simulations for terrestrial infrared instrument studies, can be applied directly for cloudy simulations. This result is relevant for future studies. Finally, the thesis includes a short study with retrieval simulations for a new sub-millimetre instrument concept.Godkänd; 2013; 20130906 (gerhol); Tillkännagivande disputation 2013-10-23 Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Gerrit Holl Ämne: Rymdteknik/Space Technology Avhandling: Remote Sensing of Ice Clouds: Synergistic Measurements and Radiative Transfer Simulations Opponent: Professor Gerald Mace, University of Utah, Salt Lake City, USA Ordförande: Professor Stefan Buehler, Institutionen för system- och rymdteknik, Luleå tekniska universitet/Meteorological Institute, Hamburg, Tyskland Tid: Fredag den 15 november 2013, kl 10.00 Plats: Aulan, Campus Kiruna, Luleå tekniska universitet</p