GEWEX water vapor assessment (G-VAP): final report

Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global.El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

While contemporary Numerical Weather Prediction models represent the large-scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small-scale features such as convective rainfall. Even though high-resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models’ initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water-related observations – especially at convective scales (also known as storm scales) – this paper discusses the observation and assimilation of water- related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective-scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water-related observations (e.g. from radar, microwave, and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud and precipitation affected observations), the need to account for the flow-dependent multivariate ‘balance’ between atmospheric water and both dynamical and mass fields, and the inherent non-Gaussian nature of atmospheric water variables

A new dynamic approach for statistical optimisation of GNSS radio occultation bending angles

Climate change has become a serious issue for our society. It is of great importance to accurately monitor climate change and provide reliable information to the society so that proper actions can be taken to alleviate the significant change of climate. Global Navigation Satellite Systems (GNSS) based radio occultation (RO) is a new satellite remote sensing technique that can provide high vertical resolution, long-term stable and global coverage atmospheric profiles of the Earth’s atmosphere. However, the quality of the retrieved atmospheric profiles decreases above about 30 km due to a low signal-to-noise ratio of GNSS signals at these high altitudes, since errors in bending angle profiles are propagated to refractivity profiles through an Abel integral and subsequently propagated to other atmospheric profiles through the hydrostatic integral. It is therefore important to carefully initialise the bending angles at high altitudes to minimise these error propagation effects and thereby optimise the climate monitoring utility of the retrieved profiles. Statistical optimisation is a commonly used method for this purpose. This method combines the observed bending angle profile and background bending angle profile based on their error covariance matrices to determine “optimised” bending angle profile. The focus of this thesis is to investigate an advanced statistical optimisation algorithm, which dynamically estimates both background and observation error covariance matrices, for the best determination of RO optimised bending angle profile. In this new algorithm, background bending angle profiles and their associated error covariance matrices are estimated using bending angles from multiple days of the European Centre for Medium-range Weather Forecasts (ECMWF) short-term (24h) forecast and analysis fields as well as the averaged observed bending angle. The background error matrices are constructed with geographically varying background error estimates on a daily-updated basis. The observation error covariance matrices are estimated using multiple days of RO data with geographically varying observation errors for an occultation event. The most distinctive advantage of the new algorithm is that both background and observation error covariance matrices are realistically estimated using large ensemble of climatological and observed data, while existing algorithms use crude formulations to estimate both error matrices. The new algorithm developed is evaluated against the algorithm used by the Wegener Center Occultation Processing System version 5.4 (OPSv5.4) by calculating statistical errors of retrieved atmospheric profiles relative to their reference profiles. Since the background errors at different heights are highly correlated and their covariance matrix is critical for the resulting optimised bending angles, the dynamically estimated background error covariance matrix is first used in statistical optimisation to retrieve atmospheric profiles from simulated MetOp as well as observed CHAMP and COSMIC RO events on single days. The dynamically estimated observation error covariance matrix is then used in the statistical optimisation together with the estimated background error covariance matrix to retrieve atmospheric profiles using the same test data. It can be concluded from the evaluation that if the estimated background error covariance matrix is solely used for the statistical optimisation, it can significantly reduce random errors and generate less or similar residual systematic errors (biases) in the optimised bending angles. The subsequent refractivity profiles and atmospheric (dry temperature) profiles retrieved are benefitted from the improved error characteristics of bending angles. If both observation and background error covariance matrices estimated from the new approach are used, the standard deviations of the optimised bending angles are only further reduced for simulated MetOp data, while for the observed CHAMP and COSMIC data, large random errors of bending angles are found at higher altitudes (e.g. > 50 km). This is likely to be that the observation errors are underestimated at high altitudes, where bending angles are largely affected by ionospheric effects and observation errors, and more weights are given to the noisy observed bending angles in the estimation of the optimised bending angles. Errors in CHAMP and COSMIC observed bending angles are further transferred downwards to their subsequently retrieved refractivity and dry temperature profiles, the quality of which is also degraded. The effects of the estimated background and observation error correlations on the atmospheric retrievals are investigated using simulated MetOp data. It is found that these realistically estimated correlations alone can reduce the random errors of the optimised bending angles significantly and improve the quality of the subsequent refractivities and temperatures. The performance of the new approach that uses only the new background matrix in the statistical optimisation on monthly occultation data is evaluated. The results show that the monthly errors are similar to those from single days, but in a smoother manner

Radiometer Calibration Using Colocated GPS Radio Occultation Measurements

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earth's limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015.United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (United States. Air Force Contract FA8721-05-C-0002

Radiometer Calibration Using Colocated GPS Radio Occultation Measurements

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earth's limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015

Tropical Temperature Variability in the UTLS: New Insights from GPS Radio Occultation Observations

AbstractGlobal positioning system (GPS) radio occultation (RO) observations, first made of Earth's atmosphere in 1995, have contributed in new ways to the understanding of the thermal structure and variability of the tropical upper troposphere–lower stratosphere (UTLS), an important component of the climate system. The UTLS plays an essential role in the global radiative balance, the exchange of water vapor, ozone, and other chemical constituents between the troposphere and stratosphere, and the transfer of energy from the troposphere to the stratosphere. With their high accuracy, precision, vertical resolution, and global coverage, RO observations are uniquely suited for studying the UTLS and a broad range of equatorial waves, including gravity waves, Kelvin waves, Rossby and mixed Rossby–gravity waves, and thermal tides. Because RO measurements are nearly unaffected by clouds, they also resolve the upper-level thermal structure of deep convection and tropical cyclones as well as volcanic clouds. Their low biases and stability from mission to mission make RO observations powerful tools for studying climate variability and trends, including the annual cycle and intraseasonal-to-interannual atmospheric modes of variability such as the quasi-biennial oscillation (QBO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO). These properties also make them useful for evaluating climate models and detection of small trends in the UTLS temperature, key indicators of climate change. This paper reviews the contributions of RO observations to the understanding of the three-dimensional structure of tropical UTLS phenomena and their variability over time scales ranging from hours to decades and longer

Variability and trends of Arctic water vapour from passive microwave satellites Special role of Polar lows and Atmospheric rivers

Water in the vapour phase is the most important component of the hydrological cycle. It is formed by processes of evaporation and sublimation during which a lot of energy as latent heat is absorbed from the atmosphere. Through atmospheric large and small scale circulation, this energy is transported and released elsewhere through the process of condensation. Water vapour is the most important greenhouse gas (GHG) due to its abundance and its effectiveness in absorbing longwave radiation. In the light of global climate change, it is of great importance to identify trends of water vapour amounts in the atmosphere and its variability. Climate change in terms of the near-surface temperature is most pronounced in the Arctic, known as Arctic Amplification. Since most of the Arctic are either open ocean or sea-ice covered surfaces, only sparse ground-based observations, mostly confined to land areas are available. Therefore, one must resort to usage of the satellite based observations which offer a great advantage by their large spatial coverage. For water vapour assessment, passive microwave satellites are well suited due to their ability to sense water vapour under clear and cloudy sky conditions independent of sun light. A number of products of integrated water vapour (IWV) from various satellites are available. However, these are often inconsistent and prone to have biases due to various assumptions and uncertainties of a priori data included in the retrieval algorithms. According to the Clausius-Clapeyron relation, water vapour is constrained by the saturation vapour pressure which is constrained only by the temperature. Therefore, this thesis investigates the hypothesis that brightness temperatures (Tbs) from spaceborne passive microwave instruments can be used as a proxy for water vapour trends. To test this hypothesis, satellites based Tbs are compared to synthetic Tbs derived from the Arctic System Reanalysis (ASR). To enable the comparison, the ASR has been evaluated in Tb space by employing the Passive and Active Microwave TRAnsfer forward model (PAMTRA). Moreover, Tbs from sounding channels were correlated with corresponding IWV based on the weighted absolute humidity profiles peaks. The hypothesis is tested for the dry, cold and sun-absent winter season (January) and the sun-return transitional spring season (May). The results show that Tbs from frequency channels can explain trends in the corresponding IWV columns derived from ASR for regions with significant positive trends for both, Tb and IWV since high correlation coefficients, reaching 0.98, have been found. This is true for different time scales, daily, monthly and for the period of 17 years (2000-2016). The exception to this has been found for May for daily time scale for frequency channel dominated by the signal from the upper troposphere lower stratosphere (UTLS). For this combination of Tbs and IWV correlations tend to be weaker and at some locations even negative. This is consistent with theoretical calculations and observational studies which report a cooling in the UTLS region for increasing IWV. However, Tbs from the corresponding channel seem less reliable in explaining trends of the corresponding IWV derived from the ASR. This indicates the importance of other processes relevant in the UTLS region during spring. Furthermore, this thesis investigates synoptic features which are associated with water vapour transport and precipitation. Previous studies have shown that Arctic cyclone activity during winter has a large impact on the sea ice melt in the following seasons making them important players in the complex feedback mechanism of the climate change in the Arctic. However, the life cycle of the most intense of such cyclones, also known as polar lows (PL) are not yet fully understood. To analyse their dynamics, this thesis investigates different environmental conditions (and their combination) between genesis and maturity stage of January PLs. PLs with overall lower thermal instability between the surface and 500 hPa during formation stage are typically accompanied by higher and steeper lapse rates throughout the boundary layer. Therefore these PLs were fostering convective development. However, as observed for a few cases, a decreased thermal instability alongside a simultaneous decrease of convection coincides with high relative humidity (mostly above 90%). Furthermore, higher relative humidity at lower levels during genesis stage promoted stronger winds at the maturity stage. Besides water vapour turnover associated with Arctic cyclones, atmospheric rivers (ARs) transport major amounts of moisture from tropical and extratropical regions into the Arctic. Studies have shown that about 90% of the total mid-latitude vertically integrated water vapour transport (IVT) is related to these synoptic features. To study the influence of ARs on PL precipitation, an event with a coupled AR and PL is compared to an event which featured only a PL. The AR had a strong influence on the PL resulting in higher snow amounts on the order of ∼ 4 kg/m2 higher wind speeds and a longer distance traveled during its life cycle, compared to the PL only case

A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a key technique for obtaining thermodynamic profiles of temperature, humidity, pressure, and density in the Earth’s troposphere. However, due to refraction effects of both the dry air and water vapor at low altitudes, retrieval of accurate profiles is challenging. Here we introduce a new moist air retrieval algorithm aiming to improve the quality of RO-retrieved profiles in moist air and including uncertainty estimation in a clear sequence of steps. The algorithm first uses RO dry temperature and pressure and background temperature/humidity and their uncertainties to retrieve humidity/temperature and their uncertainties. These temperature and humidity profiles are then combined with their corresponding background profiles by optimal estimation employing inverse-variance weighting. Finally, based on the optimally estimated temperature and humidity profiles, pressure and density profiles are computed using hydrostatic and equation-of-state formulas. The input observation and background uncertainties are dynamically estimated, accounting for spatial and temporal variations. We show results from applying the algorithm on test datasets, deriving insights from both individual profiles and statistical ensembles, and from comparison to independent 1D-Variational (1DVar) algorithm-derived moist air retrieval results from Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers. We find that the new scheme is comparable in its retrieval performance and features advantages in the integrated uncertainty estimation that includes both estimated random and systematic uncertainties and background bias correction. The new algorithm can therefore be used to obtain high-quality tropospheric climate data records including uncertainty estimation