scholarly journals The Impact of MetOp and Other Satellite Data within the Met Office Global NWP System Using an Adjoint-Based Sensitivity Method

2013 ◽  
Vol 141 (10) ◽  
pp. 3331-3342 ◽  
Author(s):  
Sangwon Joo ◽  
John Eyre ◽  
Richard Marriott
Keyword(s):  
Satellite Data ◽  
Forecast Error ◽  
Weather Prediction ◽  
Satellite System ◽  
Error Reduction ◽  
Forecast Errors ◽  
Data Types ◽  
Satellite Platform ◽  
Atmospheric Sounding ◽  
The Impact

Abstract The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various subsets of observations are compared, with emphasis on space-based observations, particularly those from instruments on board the European Organisation for the Exploitation of Meteorological Satellites Meteorological Operational-A (MetOp-A) platform. Satellite data are found to account for 64% of the short-range global forecast error reduction, with the remaining 36% coming from the assimilation of surface-based observation types. MetOp-A data are measured as having the largest impact of any individual satellite platform (about 25% of the total impact on global forecast error reduction). Their large impact, compared to that of NOAA satellites, is mainly due to MetOp's additional sensors [Infrared Atmospheric Sounding Interferometer (IASI), Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), and the Advanced Scatterometer (ASCAT)]. Microwave and hyperspectral infrared sounding techniques are found to give the largest total impacts. However, the GPS radio occultation technique is measured as having the largest mean impact per profile of observations among satellite types. This study demonstrates how the FSO technique can be used to assess the impact of individual satellite data types in NWP. The calculated impacts can be used to guide improvements in the use of currently available data and to contribute to discussions on the evolution of future observing systems.

scholarly journals The Radiometric Sensitivity Requirements for Satellite Microwave Temperature Sounding Instruments for Numerical Weather Prediction

2010 ◽  
Vol 27 (3) ◽  
pp. 443-456 ◽  
Author(s):  
William Bell ◽  
Sabatino Di Michele ◽  
Peter Bauer ◽  
Tony McNally ◽  
Stephen J. English ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Forecast Errors ◽  
Atmospheric Infrared Sounder ◽  
Microwave Sounding Unit ◽  
Atmospheric Sounding ◽  
Microwave Sounding ◽  
Satellite Microwave ◽  
The Impact

Abstract The sensitivity of NWP forecast accuracy with respect to the radiometric performance of microwave sounders is assessed through a series of observing system experiments at the Met Office and ECMWF. The observing system experiments compare the impact of normal data from a single Advanced Microwave Sounding Unit (AMSU) with that from an AMSU where synthetic noise has been added. The results show a measurable reduction in forecast improvement in the Southern Hemisphere, with improvements reduced by 11% for relatively small increases in radiometric noise [noise-equivalent brightness temperature (NEΔT) increased from 0.1 to 0.2 K for remapped data]. The impact of microwave sounding data is shown to be significantly less than was the case prior to the use of advanced infrared sounder data [Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI)], with microwave sounding data now reducing Southern Hemisphere forecast errors by approximately 10% compared to 40% in the pre-AIRS/IASI period.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

scholarly journals Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

2016 ◽  
Author(s):  
Emlyn M. Jones ◽  
Mark E. Baird ◽  
Mathieu Mongin ◽  
John Parslow ◽  
Jenny Skerratt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Forecast Error ◽  
Weather Prediction ◽  
Biogeochemical Model ◽  
Forecast Errors ◽  
Barrier Reef ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Assimilation System

Abstract. Skilful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically-derived relationships between IOPs and variables such as Chlorophyll-a concentration (Chl-a), Total Suspended Solids (TSS) and Color Dissolved Organic Matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due the additional signal from bottom reflectance. Rather than assimilate quantities calculated using error-prone IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance. The assimilation of a directly-observed quantity, in this case remote-sensing reflectance, is analogous to the assimilation of temperature brightness in Numerical Weather Prediction (NWP), or along-track sea-surface height in hydrodynamic models. To assimilate the observed reflectance, we use an in-water optical model to produce an equivalent simulated remote-sensing reflectance, and calculate the mis-match between the observed and simulated quantities to constrain the BGC model with a Deterministic Ensemble Kalman Filter (DEnKF). Using the assumption that simulated surface Chl-a is equivalent to remotely-sensed OC3M estimate of Chl-a resulted in a forecast error of approximately 75 %. Alternatively, assimilation of remote-sensing reflectance resulted in a forecast error of less than 40 %. Thus, in the coastal waters of the GBR, assimilating remote-sensing reflectance halved the forecast errors. When the analysis and forecast fields from the assimilation system are compared with the non-assimilating model, an independent comparison to in-situ observations of Chl-a, TSS, and dissolved inorganic nutrients (NO3, NH4 and DIP) show that errors are reduced by up to 90 %. In all cases, the assimilation system improves the result compared to the non-assimilating model. This approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially-derived TSS and CDOM, or the lack of a calibrated regional IOP algorithm.

scholarly journals Diagnosing the Origin of Extended-Range Forecast Errors

2010 ◽  
Vol 138 (6) ◽  
pp. 2434-2446 ◽  
Author(s):  
T. Jung ◽  
M. J. Miller ◽  
T. N. Palmer
Keyword(s):  
North America ◽  
Northern Hemisphere ◽  
North Pacific ◽  
Forecast Error ◽  
Error Reduction ◽  
Forecast Errors ◽  
The North ◽  
Extended Range ◽  
Ecmwf Model ◽  
The North Pacific

Abstract Experiments with the ECMWF model are carried out to study the influence that a correct representation of the lower boundary conditions, the tropical atmosphere, and the Northern Hemisphere stratosphere would have on extended-range forecast skill of the extratropical Northern Hemisphere troposphere during boreal winter. Generation of forecast errors during the course of the integration is artificially reduced by relaxing the ECMWF model toward the 40-yr ECMWF Re-Analysis (ERA-40) in certain regions. Prescribing rather than persisting sea surface temperature and sea ice fields leads to a modest forecast error reduction in the extended range, especially over the North Pacific and North America; no beneficial influence is found in the medium range. Relaxation of the tropical troposphere leads to reduced extended-range forecast errors especially over the North Pacific, North America, and the North Atlantic. It is shown that a better representation of the Madden–Julian oscillation is of secondary importance for explaining the results of the tropical relaxation experiments. The influence from the tropical stratosphere is negligible. Relaxation of the Northern Hemisphere stratosphere leads to forecast error reduction primarily in high latitudes and over Europe. However, given the strong influence from the troposphere onto the Northern Hemisphere stratosphere it is argued that stratospherically forced experiments are very difficult to interpret in terms of their implications for extended-range predictability of the tropospheric flow. The results are discussed in the context of future forecasting system development.

scholarly journals A simulated observation database to assess the impact of the IASI-NG hyperspectral infrared sounder

2018 ◽  
Vol 11 (2) ◽  
pp. 803-818 ◽  
Author(s):  
Javier Andrey-Andrés ◽  
Nadia Fourrié ◽  
Vincent Guidard ◽  
Raymond Armante ◽  
Pascal Brunel ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Weather Prediction ◽  
Simulated Data ◽  
The Public ◽  
Impact Studies ◽  
New Instrument ◽  
Atmospheric Sounding ◽  
Common Framework ◽  
New Generation ◽  
The Impact

Abstract. The highly accurate measurements of the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) are used in numerical weather prediction (NWP), atmospheric chemistry and climate monitoring. As the second generation of the European Polar System (EPS-SG) is being developed, a new generation of IASI instruments has been designed to fly on board the MetOp-SG constellation: IASI New Generation (IASI-NG). In order to prepare the arrival of this new instrument, and to evaluate its impact on NWP and atmospheric chemistry applications, a set of IASI and IASI-NG simulated data was built and made available to the public to set a common framework for future impact studies. This paper describes the information available in this database and the procedure followed to run the IASI and IASI-NG simulations. These simulated data were evaluated by comparing IASI-NG to IASI observations. The result is also presented here. Additionally, preliminary impact studies of the benefit of IASI-NG compared to IASI on the retrieval of temperature and humidity in a NWP framework are also shown in the present work. With a channel dataset located in the same wave numbers for both instruments, we showed an improvement of the temperature retrievals throughout the atmosphere, with a maximum in the troposphere with IASI-NG and a lower benefit for the tropospheric humidity.

scholarly journals Preliminary validation of refractivity from a new radio occultation sounder GNOS/FY-3C

2015 ◽  
Vol 8 (9) ◽  
pp. 9009-9044 ◽  
Author(s):  
M. Liao ◽  
P. Zhang ◽  
G. L. Yang ◽  
Y. M. Bi ◽  
Y. Liu ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Satellite System ◽  
Navigation Satellite ◽  
Near Surface ◽  
Vertical Range ◽  
New Radio ◽  
Atmospheric Sounding ◽  
Global Navigation Satellite

Abstract. As a new member of space-based radio occultation sounder, the GNOS (Global Navigation Satellite System Occultation Sounder) mounted on FY-3C has been carrying out the atmospheric sounding since 23 September 2013. GNOS takes a daily measurement up to 800 times with GPS (Global Position System) and Chinese BDS (BeiDou navigation satellite) signals. The refractivity profiles from GNOS are compared with the co-located ECMWF (European Centre for Medium-Range Weather Forecasts) analyses in this paper. Bias and standard deviation have being calculated as the function of altitude. The mean bias is about 0.2 % from the near surface to 35 km. The average standard deviation is within 2 % while it is down to about 1 % in the range 5–30 km where best soundings are usually made. To evaluate the performance of GNOS, COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) and GRAS/METOP-A (GNSS Receiver for Atmospheric Sounding) data are also compared to ECMWF analyses as the reference. The results show that GNOS/FY-3C meets the requirements of the design well. It possesses a sounding capability similar to COSMIC and GRAS in the vertical range of 0–30 km, though it needs improvement in higher altitude. Generally, it provides a new data source for global NWP (numerical weather prediction) community.

scholarly journals A simulated observation database to assess the impact of IASI-NG hyperspectral infrared sounder

2017 ◽  
Author(s):  
Javier Andrey-Andrés ◽  
Nadia Fourrié ◽  
Vincent Guidard ◽  
Raymond Armante ◽  
Pascal Brunel ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Weather Prediction ◽  
Simulated Data ◽  
The Public ◽  
Impact Studies ◽  
New Instrument ◽  
Atmospheric Sounding ◽  
Common Framework ◽  
New Generation ◽  
The Impact

Abstract. The highly accurate measurements of the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) are used in Numerical Weather Prediction (NWP), atmospheric chemistry and climate monitoring. As the second generation of the European Polar System (EPS-SG) is being developed, a new generation of IASI instruments has been designed to fly on board the MetOp-SG constellation: IASI New Generation (IASI-NG). In order to prepare the arrival of this new instrument, and to evaluate its impact on NWP and atmospheric chemistry applications, a set of IASI and IASI-NG simulated data was built and made available to the public to set a common framework for future impact studies. This paper describes the information available in this database and the procedure followed to run the IASI and IASI-NG simulations. These simulated data were evaluated by comparing IASI-NG to IASI observations. The result is also presented here. Additionally, preliminary impact studies of the benefit of IASI-NG compared to IASI on the retrieval of temperature and humidity in a NWP framework are also shown in the present work. With a channel dataset located in the same wave numbers for both instruments, we showed an improvement of the temperature retrievals along all the atmosphere with a maximum in the troposphere with IASI-NG and a lower benefit for the tropospheric humidity.

scholarly journals Effect of boundary conditions on adjoint-based forecast sensitivity observation impact in a regional model

2021 ◽  
Author(s):  
Hyun Mee Kim ◽  
Dae-Hui Kim
Keyword(s):  
Boundary Conditions ◽  
Forecast Error ◽  
Error Reduction ◽  
Regional Model ◽  
Buffer Size ◽  
Lateral Boundary ◽  
Modeling Framework ◽  
Lateral Boundary Conditions ◽  
Observation Impact ◽  
The Impact

AbstractIn this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

scholarly journals Data assimilation of GNSS zenith total delays from a Nordic processing centre

2017 ◽  
Vol 17 (22) ◽  
pp. 13983-13998 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

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