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- Author or Editor: Shakeel Asharaf x
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Abstract
Indian summer monsoon rainfall was examined in two different greenhouse gas emission scenarios: the Special Report on Emissions Scenarios (SRES; B1) and a similar greenhouse gas scenario, the new representative concentration pathways (RCPs; RCP4.5). The rainfall change in the climate model projections through remotely induced changes in precipitation processes and through changes in precipitation efficiency processes was discussed. To that end, two model setups were applied: 1) the regional climate model (RCM) Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM), nested in the global climate model (GCM) ECHAM5/Max Planck Institute ocean model (ECHAM5/MPIOM), applying the greenhouse gas scenario B1; and 2) the RCM nested in a newer version of the GCM, ECHAM6/MPIOM, incorporating the RCP4.5 scenario. Both GCM simulations showed a slight increase in precipitation over central India toward the end of the twenty-first century. This slight increase was the result of two largely compensating changes: increase of remotely induced precipitation and decrease of precipitation efficiency. The RCM with the scenario RCP4.5 followed this trend, but with smaller changes. However, the RCM with B1 showed a decreasing trend in precipitation because of a slightly larger absolute change of the reduced precipitation efficiency compared to the change caused by the remote processes. Changes of these processes in the scenario simulations were larger than the natural variability, as simulated in an unperturbed preindustrial greenhouse gas control (CTL) climate simulation. Results indicated that the projection of the Indian summer monsoon rainfall is still a key challenge for both the GCM and the RCM.
Abstract
Indian summer monsoon rainfall was examined in two different greenhouse gas emission scenarios: the Special Report on Emissions Scenarios (SRES; B1) and a similar greenhouse gas scenario, the new representative concentration pathways (RCPs; RCP4.5). The rainfall change in the climate model projections through remotely induced changes in precipitation processes and through changes in precipitation efficiency processes was discussed. To that end, two model setups were applied: 1) the regional climate model (RCM) Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM), nested in the global climate model (GCM) ECHAM5/Max Planck Institute ocean model (ECHAM5/MPIOM), applying the greenhouse gas scenario B1; and 2) the RCM nested in a newer version of the GCM, ECHAM6/MPIOM, incorporating the RCP4.5 scenario. Both GCM simulations showed a slight increase in precipitation over central India toward the end of the twenty-first century. This slight increase was the result of two largely compensating changes: increase of remotely induced precipitation and decrease of precipitation efficiency. The RCM with the scenario RCP4.5 followed this trend, but with smaller changes. However, the RCM with B1 showed a decreasing trend in precipitation because of a slightly larger absolute change of the reduced precipitation efficiency compared to the change caused by the remote processes. Changes of these processes in the scenario simulations were larger than the natural variability, as simulated in an unperturbed preindustrial greenhouse gas control (CTL) climate simulation. Results indicated that the projection of the Indian summer monsoon rainfall is still a key challenge for both the GCM and the RCM.
Abstract
Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.
Abstract
Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.
Updates on CYGNSS Ocean Surface Wind Validation in the TropicsAbstract
Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.
Abstract
Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.
Abstract
The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.
Abstract
The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.
CYGNSS Ocean Surface Wind Validation in the TropicsAbstract
Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.
Abstract
Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.
Abstract
The ability of four regional climate models (RCMs) to represent the Indian monsoon was verified in a consistent framework for the period 1981–2000 using the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as lateral boundary forcing data. During the monsoon period, the RCMs are able to capture the spatial distribution of precipitation with a maximum over the central and west coast of India, but with important biases at the regional scale on the east coast of India in Bangladesh and Myanmar. Most models are too warm in the north of India compared to the observations. This has an impact on the simulated mean sea level pressure from the RCMs, being in general too low compared to ERA-40. Those biases perturb the land–sea temperature and pressure contrasts that drive the monsoon dynamics and, as a consequence, lead to an overestimation of wind speed, especially over the sea. The timing of the monsoon onset of the RCMs is in good agreement with the one obtained from observationally based gridded datasets, while the monsoon withdrawal is less well simulated. A Hovmöller diagram representation of the mean annual cycle of precipitation reveals that the meridional motion of the precipitation simulated by the RCMs is comparable to the one observed, but the precipitation amounts and the regional distribution differ substantially between the four RCMs. In summary, the spread at the regional scale between the RCMs indicates that important feedbacks and processes are poorly, or not, taken into account in the state-of-the-art regional climate models.
Abstract
The ability of four regional climate models (RCMs) to represent the Indian monsoon was verified in a consistent framework for the period 1981–2000 using the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as lateral boundary forcing data. During the monsoon period, the RCMs are able to capture the spatial distribution of precipitation with a maximum over the central and west coast of India, but with important biases at the regional scale on the east coast of India in Bangladesh and Myanmar. Most models are too warm in the north of India compared to the observations. This has an impact on the simulated mean sea level pressure from the RCMs, being in general too low compared to ERA-40. Those biases perturb the land–sea temperature and pressure contrasts that drive the monsoon dynamics and, as a consequence, lead to an overestimation of wind speed, especially over the sea. The timing of the monsoon onset of the RCMs is in good agreement with the one obtained from observationally based gridded datasets, while the monsoon withdrawal is less well simulated. A Hovmöller diagram representation of the mean annual cycle of precipitation reveals that the meridional motion of the precipitation simulated by the RCMs is comparable to the one observed, but the precipitation amounts and the regional distribution differ substantially between the four RCMs. In summary, the spread at the regional scale between the RCMs indicates that important feedbacks and processes are poorly, or not, taken into account in the state-of-the-art regional climate models.
