Assessments of Assimilation of TEMPEST-D into the NCEP Global Forecast System

2020 ◽  
Author(s):  
Ting-Chi Wu ◽  
Milija Zupanski ◽  
Lewis Grasso ◽  
James Fluke ◽  
Heather Cronk ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Control Experiment ◽  
Weather Forecasting ◽  
Cost Effective ◽  
Global Forecast System ◽  
Forecast System ◽  
Research Project ◽  
Microwave Radiometers ◽  
The Impact ◽  
Ncep Global Forecast System

<p>Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established.</p><p>One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D.</p><p>By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.  </p>

scholarly journals Ensemble Data Assimilation with the NCEP Global Forecast System

2008 ◽  
Vol 136 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Thomas M. Hamill ◽  
Xue Wei ◽  
Yucheng Song ◽  
Zoltan Toth
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Global Forecast System ◽  
Forecast System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Triangular Truncation ◽  
Data Assimilation System ◽  
Ncep Global Forecast System ◽  
Assimilation System

Abstract Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP–NCAR reanalysis to each ensemble member (additive inflation) performed slightly better than the other two methods (multiplicative inflation and relaxation-to-prior).

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

scholarly journals A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 4098-4117 ◽  
Author(s):  
Xuguang Wang ◽  
David Parrish ◽  
Daryl Kleist ◽  
Jeffrey Whitaker
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Coupled System ◽  
Global Forecast System ◽  
Forecast System ◽  
Outer Loop ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
The One ◽  
Ncep Global Forecast System

Abstract An ensemble Kalman filter–variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1–5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence.

GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System

2014 ◽  
Vol 142 (9) ◽  
pp. 3303-3325 ◽  
Author(s):  
Xuguang Wang ◽  
Ting Lei
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Negative Impact ◽  
Lead Times ◽  
Multiple Time ◽  
Global Forecast System ◽  
Forecast System ◽  
Variational Minimization ◽  
Ncep Global Forecast System

A four-dimensional (4D) ensemble–variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

scholarly journals Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

2016 ◽  
Vol 113 (42) ◽  
pp. 11765-11769 ◽  
Author(s):  
Banglin Zhang ◽  
Richard S. Lindzen ◽  
Vijay Tallapragada ◽  
Fuzhong Weng ◽  
Qingfu Liu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Forecast Model ◽  
Vertical Resolution ◽  
Global Forecast System ◽  
Forecast System ◽  
Atlantic Basin ◽  
Hurricane Track ◽  
Environmental Prediction ◽  
The Impact ◽  
Positive Impacts

The atmosphere−ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels.

scholarly journals Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 74
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Bai Li ◽  
Jiajia Mao ◽  
Dawei Lin
Keyword(s):  
Control Experiment ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Radar Data ◽  
Specific Humidity ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Microwave Radiometers ◽  
The Impact ◽  
Precipitation Enhancement

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

scholarly journals Impact of Boreal Summer Intra-Seasonal Oscillations on the Heavy Rainfall Events in Taiwan during the 2017 Meiyu Season

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 205 ◽  
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jen-Her Chen ◽  
Liping Deng
Keyword(s):  
Heavy Rainfall ◽  
Boreal Summer ◽  
Economic Losses ◽  
Global Forecast System ◽  
Forecast System ◽  
Rainfall Events ◽  
The Impact ◽  
Meiyu Season ◽  
Heavy Rainfall Events ◽  
Seasonal Oscillations

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.

Sign in / Sign up

Export Citation Format

Share Document