scholarly journals A Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

2018 ◽  
Vol 146 (7) ◽  
pp. 2221-2236 ◽  
Author(s):  
Bachir Annane ◽  
Brian McNoldy ◽  
S. Mark Leidner ◽  
Ross Hoffman ◽  
Robert Atlas ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite System ◽  
Analysis Method ◽  
Short Term ◽  
Background Error ◽  
Wind Speeds ◽  
Ocean Surface Winds ◽  
Global Navigation Satellite ◽  
Data Assimilation System

Abstract In preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA’s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (12) ◽  
pp. 3563-3576 ◽  
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Background Error ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

scholarly journals Variational Analysis of Simulated Ocean Surface Winds from the Cyclone Global Navigation Satellite System (CYGNSS) and Evaluation Using a Regional OSSE

2018 ◽  
Vol 35 (8) ◽  
pp. 1571-1584 ◽  
Author(s):  
S. Mark Leidner ◽  
Bachir Annane ◽  
Brian McNoldy ◽  
Ross Hoffman ◽  
Robert Atlas
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Global Navigation Satellite System ◽  
Wind Field ◽  
Variational Analysis ◽  
Satellite System ◽  
Navigation Satellite ◽  
Directional Information ◽  
Wind Speeds ◽  
Global Navigation Satellite

AbstractA positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.

scholarly journals Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

2021 ◽  
Vol 13 (24) ◽  
pp. 5110
Author(s):  
Lucrezia Ricciardulli ◽  
Carl Mears ◽  
Andrew Manaster ◽  
Thomas Meissner
Keyword(s):  
Tropical Cyclones ◽  
Satellite System ◽  
Tropical Meteorology ◽  
The Public ◽  
Wind Speeds ◽  
Wind Analysis ◽  
Ocean Surface Winds ◽  
Wind Regimes ◽  
Global Navigation Satellite ◽  
High Winds

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.

scholarly journals Galileo L10 Satellites: Orbit, Clock and Signal-in-Space Performance Analysis

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1695
Author(s):  
Constantin-Octavian Andrei ◽  
Sonja Lahtinen ◽  
Markku Poutanen ◽  
Hannu Koivula ◽  
Jan Johansson
Keyword(s):  
Performance Indicators ◽  
Satellite System ◽  
Short Term ◽  
Orbital Inclination ◽  
Navigation Data ◽  
Periodic Signals ◽  
Global Navigation Satellite ◽  
Minor Correction ◽  
Very High

The tenth launch (L10) of the European Global Navigation Satellite System Galileo filled in all orbital slots in the constellation. The launch carried four Galileo satellites and took place in July 2018. The satellites were declared operational in February 2019. In this study, we report on the performance of the Galileo L10 satellites in terms of orbital inclination and repeat period parameters, broadcast satellite clocks and signal in space (SiS) performance indicators. We used all available broadcast navigation data from the IGS consolidated navigation files. These satellites have not been reported in the previous studies. First, the orbital inclination (56.7±0.15°) and repeat period (50680.7±0.22 s) for all four satellites are within the nominal values. The data analysis reveals also 13.5-, 27-, 177- and 354-days periodic signals. Second, the broadcast satellite clocks show different correction magnitude due to different trends in the bias component. One clock switch and several other minor correction jumps have occurred since the satellites were declared operational. Short-term discontinuities are within ±1 ps/s, whereas clock accuracy values are constantly below 0.20 m (root-mean-square—rms). Finally, the SiS performance has been very high in terms of availability and accuracy. Monthly SiS availability has been constantly above the target value of 87% and much higher in 2020 as compared to 2019. Monthly SiS accuracy has been below 0.20 m (95th percentile) and below 0.40 m (99th percentile). The performance figures depend on the content and quality of the consolidated navigation files as well as the precise reference products. Nevertheless, these levels of accuracy are well below the 7 m threshold (95th percentile) specified in the Galileo service definition document.

scholarly journals Inventory of Locations of Old Mining Works Using LiDAR Data: A Case Study in Slovakia

2021 ◽  
Vol 13 (12) ◽  
pp. 6981
Author(s):  
Marcela Bindzarova Gergelova ◽  
Slavomir Labant ◽  
Jozef Mizak ◽  
Pavel Sustek ◽  
Lubomir Leicher
Keyword(s):  
Satellite System ◽  
Precise Determination ◽  
Lidar Data ◽  
Positional Accuracy ◽  
Current Form ◽  
Mining History ◽  
Mining Works ◽  
Gnss Measurements ◽  
Global Navigation Satellite

The concept of further sustainable development in the area of administration of the register of old mining works and recent mining works in Slovakia requires precise determination of the locations of the objects that constitute it. The objects in this register have their uniqueness linked with the history of mining in Slovakia. The state of positional accuracy in the registration of objects in its current form is unsatisfactory. Different database sources containing the locations of the old mining works are insufficient and show significant locational deviations. For this reason, it is necessary to precisely locate old mining works using modern measuring technologies. The most effective approach to solving this problem is the use of LiDAR data, which at the same time allow determining the position and above-ground shape of old mining works. Two localities with significant mining history were selected for this case study. Positional deviations in the location of old mining works among the selected data were determined from the register of old mining works in Slovakia, global navigation satellite system (GNSS) measurements, multidirectional hill-shading using LiDAR, and accessible data from the open street map. To compare the positions of identical old mining works from the selected database sources, we established differences in the coordinates (ΔX, ΔY) and calculated the positional deviations of the same objects. The average positional deviation in the total count of nineteen objects comparing documents, LiDAR data, and the register was 33.6 m. Comparing the locations of twelve old mining works between the LiDAR data and the open street map, the average positional deviation was 16.3 m. Between the data sources from GNSS and the registry of old mining works, the average positional deviation of four selected objects was 39.17 m.

scholarly journals HEIGHT DETERMINATION TECHNIQUES FOR THE NEXT NATIONAL HEIGHT SYSTEM OF FINLAND - A CASE STUDY

2015 ◽  
Vol 41 (4) ◽  
pp. 145-155
Author(s):  
Timo Saari ◽  
Markku Poutanen ◽  
Veikko Saaranen ◽  
Harri Kaartinen ◽  
Antero Kukko ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Pilot Project ◽  
Satellite System ◽  
Reference Station ◽  
Virtual Reference ◽  
Precise Levelling ◽  
Height Determination ◽  
Weak Points ◽  
Global Navigation Satellite

Precise levelling is known for its accuracy and reliability in height determination, but the process itself is slow, laborious and expensive. We have started a project to study methods for height determination that could decrease the creation time of national height systems without losing the accuracy and reliability that is needed for them. In the pilot project described here, we study some of the alternative techniques with a pilot field test where we compared them with the precise levelling. The purpose of the test is not to evaluate the mutual superiority or suitability of the techniques, but to establish the background for a larger test and to find strong and weak points of each technique. The techniques chosen for this study were precise levelling, Mobile Laser Scanning (MLS) and Global Navigation Satellite System (GNSS) levelling, which included static Global Positioning System (GPS) and Virtual Reference Station (VRS) measurements. This research highlighted the differences of the studied techniques and gave insights about the framework and procedure for the later experiments. The research will continue in a larger scale, where the suitability of the techniques regarding the height systems is to be determined.

scholarly journals Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

Sign in / Sign up

Export Citation Format

Share Document