Assimilation of Surface Pressure Observations Using an Ensemble Filter in an Idealized Global Atmospheric Prediction System

2005 ◽  
Vol 62 (8) ◽  
pp. 2925-2938 ◽  
Author(s):  
Jeffrey L. Anderson ◽  
Bruce Wyman ◽  
Shaoqing Zhang ◽  
Timothy Hoar
Keyword(s):  
Surface Pressure ◽  
Temporal Frequency ◽  
Surface Wind ◽  
Spatial Density ◽  
State Variables ◽  
Perfect Model ◽  
Prior Estimate ◽  
Model Context ◽  
Assimilation System ◽  
Error Observation

Abstract An ensemble filter data assimilation system is tested in a perfect model setting using a low resolution Held–Suarez configuration of an atmospheric GCM. The assimilation system is able to reconstruct details of the model’s state at all levels when only observations of surface pressure (PS) are available. The impacts of varying the spatial density and temporal frequency of PS observations are examined. The error of the ensemble mean assimilation prior estimate appears to saturate at some point as the number of PS observations available once every 24 h is increased. However, increasing the frequency with which PS observations are available from a fixed network of 1800 randomly located stations results in an apparently unbounded decrease in the assimilation’s prior error for both PS and all other model state variables. The error reduces smoothly as a function of observation frequency except for a band with observation periods around 4 h. Assimilated states are found to display enhanced amplitude high-frequency gravity wave oscillations when observations are taken once every few hours, and this adversely impacts the assimilation quality. Assimilations of only surface temperature and only surface wind components are also examined. The results indicate that, in a perfect model context, ensemble filters are able to extract surprising amounts of information from observations of only a small portion of a model’s spatial domain. This suggests that most of the remaining challenges for ensemble filter assimilation are confined to problems such as model error, observation representativeness error, and unknown instrument error characteristics that are outside the scope of perfect model experiments. While it is dangerous to extrapolate from these simple experiments to operational atmospheric assimilation, the results also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.

scholarly journals Impacts of Frequent Assimilation of Surface Pressure Observations on Atmospheric Analyses

2014 ◽  
Vol 142 (12) ◽  
pp. 4477-4483 ◽  
Author(s):  
Lili Lei ◽  
Jeffrey L. Anderson
Keyword(s):  
Surface Pressure ◽  
Temporal Frequency ◽  
State Variables ◽  
Localization Function ◽  
Community Atmosphere Model ◽  
Entire Depth ◽  
Analysis Error ◽  
Mean Square Difference ◽  
Optimal Localization ◽  
Vertical Localization

Abstract To investigate the impacts of frequently assimilating only surface pressure (PS) observations, the Data Assimilation Research Testbed and the Community Atmosphere Model (DART/CAM) are used for observing system simulation experiments with the ensemble Kalman filter. An empirical localization function (ELF) is used to effectively spread the information from PS in the vertical. The ELF minimizes the root-mean-square difference between the truth and the posterior ensemble mean for state variables. The temporal frequency of the observations is increased from 6 to 3 h, and then 1 h. By observing only PS, the uncertainty throughout the entire depth of the troposphere can be constrained. The analysis error over the entire depth of the troposphere, especially the middle troposphere, is reduced with increased assimilation frequency. The ELF is similar to the vertical localization function used in the Twentieth-Century Reanalysis (20CR); thus, it demonstrates that the current vertical localization in the 20CR is close to the optimal localization function.

scholarly journals Importance of Forecast Error Multivariate Correlations in Idealized Assimilations of GPS Radio Occultation Data with the Ensemble Adjustment Filter

2007 ◽  
Vol 135 (1) ◽  
pp. 173-185 ◽  
Author(s):  
Hui Liu ◽  
Jeffrey L. Anderson ◽  
Ying-Hwa Kuo ◽  
Kevin Raeder
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Radio Occultation ◽  
Forecast Error ◽  
Atmospheric Model ◽  
Specific Humidity ◽  
State Variables ◽  
Perfect Model ◽  
Occultation Data ◽  
Multivariate Correlations

Abstract The importance of multivariate forecast error correlations between specific humidity, temperature, and surface pressure in perfect model assimilations of Global Positioning System radio occultation (RO) refractivity data is examined using the Ensemble Adjustment Filter (EAF) and the NCAR global Community Atmospheric Model, version 3. The goal is to explore whether inclusion of the multivariate forecast error correlations in the background term of 3D and 4D variational data assimilation systems (3DVAR and 4DVAR, respectively) is likely to improve RO data assimilation in the troposphere. It is not possible to explicitly neglect multivariate forecast error correlations with the EAF because they are not used directly in the algorithm. Instead, the filter only makes use of the forecast error correlations between observed quantities (RO here) and model state variables. However, because the forecast error correlations for RO observations are dominated by correlations with a subset of state variable types in certain regions, the importance of multivariate forecast error correlations between state variables can be indirectly assessed. This is done by setting the forecast error correlations of RO observations and some state variables (e.g., temperature) to zero in a set of assimilation experiments. Comparing these experiments to a control in which all state variables are impacted by RO observations allows an indirect assessment of the importance of multivariate correlations between state variables not impacted by the observations and those that are impacted. Results suggest that proper specification of the multivariate forecast error correlations in 3DVAR and 4DVAR systems should improve the analysis of specific humidity, surface pressure, and temperature in the troposphere when assimilating RO data.

scholarly journals A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

2010 ◽  
Vol 25 (3) ◽  
pp. 931-949 ◽  
Author(s):  
Li Bi ◽  
James A. Jung ◽  
Michael C. Morgan ◽  
John F. Le Marshall
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Temperature Fields ◽  
Experimental Simulation ◽  
Naval Research ◽  
Surface Winds ◽  
Control Simulation ◽  
Wind Retrievals ◽  
Global Data ◽  
Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

scholarly journals A Diagnostic Study of the Intensity of Three Tropical Cyclones in the Australian Region. Part II: An Analytic Method for Determining the Time Variation of the Intensity of a Tropical Cyclone*

2010 ◽  
Vol 138 (1) ◽  
pp. 22-41 ◽  
Author(s):  
France Lajoie ◽  
Kevin Walsh
Keyword(s):  
Boundary Layer ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Surface Pressure ◽  
Time Variation ◽  
Surface Wind ◽  
Ground Truth Data ◽  
Maximum Wind ◽  
Australian Region ◽  
Central Surface

Abstract The observed features discussed in Part I of this paper, regarding the intensification and dissipation of Tropical Cyclone Kathy, have been integrated in a simple mathematical model that can produce a reliable 15–30-h forecast of (i) the central surface pressure of a tropical cyclone, (ii) the sustained maximum surface wind and gust around the cyclone, (iii) the radial distribution of the sustained mean surface wind along different directions, and (iv) the time variation of the three intensity parameters previously mentioned. For three tropical cyclones in the Australian region that have some reliable ground truth data, the computed central surface pressure, the predicted maximum mean surface wind, and maximum gust were, respectively, within ±3 hPa and ±2 m s−1 of the observations. Since the model is only based on the circulation in the boundary layer and on the variation of the cloud structure in and around the cyclone, its accuracy strongly suggests that (i) the maximum wind is partly dependent on the large-scale environmental circulation within the boundary layer and partly on the size of the radius of maximum wind and (ii) that all factors that contribute one way or another to the intensity of a tropical cyclone act together to control the size of the eye radius and the central surface pressure.

Combined state-parameter estimation with the LETKF for convective-scale weather forecasting

2020 ◽  
Author(s):  
Yvonne Ruckstuhl ◽  
Tijana Janjic
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Roughness Length ◽  
Surface Wind ◽  
Model Error ◽  
Surface Fluxes ◽  
Model Parameters ◽  
State Variables ◽  
Augmented State ◽  
The Impact

<p>We investigate the feasibility of addressing model error by perturbing and  estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are updated by observations via their correlation with observed state variables. This online approach offers a flexible, yet consistent way to better fit model variables affected by the chosen parameters to observations, while ensuring feasible model states. We show in a nearly-operational convection-permitting configuration that the prediction of clouds and precipitation with the COSMO-DE model is improved if the two dimensional roughness length parameter is estimated with the augmented state approach. Here, the targeted model error is the roughness length itself and the surface fluxes, which influence the initiation of convection. At analysis time, Gaussian noise with a specified correlation matrix is added to the roughness length to regulate the parameter spread. In the northern part of the COSMO-DE domain, where the terrain is mostly flat and assimilated surface wind measurements are dense, estimating the roughness length led to improved forecasts of up to six hours of clouds and precipitation. In the southern part of the domain, the parameter estimation was detrimental unless the correlation length scale of the Gaussian noise that is added to the roughness length is increased. The impact of the parameter estimation was found to be larger when synoptic forcing is weak and the model output is more sensitive to the roughness length.</p>

scholarly journals Latent Heat Flux Sensitivity to Sea Surface Temperature: Regional Perspectives

2017 ◽  
Vol 30 (1) ◽  
pp. 129-143 ◽  
Author(s):  
B. Praveen Kumar ◽  
Meghan F. Cronin ◽  
Sudheer Joseph ◽  
M. Ravichandran ◽  
N. Sureshkumar
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Global Analysis ◽  
Surface Wind ◽  
Sea Surface ◽  
State Variables ◽  
Monsoon Period

A global analysis of latent heat flux (LHF) sensitivity to sea surface temperature (SST) is performed, with focus on the tropics and the north Indian Ocean (NIO). Sensitivity of LHF state variables (surface wind speed Ws and vertical humidity gradients Δq) to SST give rise to mutually interacting dynamical (Ws driven) and thermodynamical (Δq driven) coupled feedbacks. Generally, LHF sensitivity to SST is pronounced over tropics where SST increase causes Ws (Δq) changes, resulting in a maximum decrease (increase) of LHF by ~15 W m−2 (°C)−1. But the Bay of Bengal (BoB) and north Arabian Sea (NAS) remain an exception that is opposite to the global feedback relationship. This uniqueness is attributed to strong seasonality in monsoon Ws and Δq variations, which brings in warm (cold) continental air mass into the BoB and NAS during summer (winter), producing a large seasonal cycle in air–sea temperature difference ΔT (and hence in Δq). In other tropical oceans, surface air is mostly of marine origin and blows from colder to warmer waters, resulting in a constant ΔT ~ 1°C throughout the year, and hence a constant Δq. Thus, unlike other basins, when the BoB and NAS are warming, air temperature warms faster than SST. The resultant decrease in ΔT and Δq contributes to decrease the LHF with increased SST, contrary to other basins. This analysis suggests that, in the NIO, LHF variability is largely controlled by thermodynamic processes, which peak during the monsoon period. These observed LHF sensitivities are then used to speculate how the surface energetics and coupled feedbacks may change in a warmer world.

scholarly journals Central and Eastern U.S. Surface Pressure Variations Derived from the USArray Network

2015 ◽  
Vol 143 (4) ◽  
pp. 1472-1493 ◽  
Author(s):  
Alexander A. Jacques ◽  
John D. Horel ◽  
Erik T. Crosman ◽  
Frank L. Vernon
Keyword(s):  
United States ◽  
Great Plains ◽  
Surface Pressure ◽  
Surface Wind ◽  
Pressure Sensors ◽  
Storm Track ◽  
The United States ◽  
Large Magnitude ◽  
Wide Range ◽  
Pressure Perturbations

Abstract Large-magnitude pressure signatures associated with a wide range of atmospheric phenomena (e.g., mesoscale gravity waves, convective complexes, tropical disturbances, and synoptic storm systems) are examined using a unique set of surface pressure sensors deployed as part of the National Science Foundation EarthScope USArray Transportable Array. As part of the USArray project, approximately 400 seismic stations were deployed in a pseudogrid fashion across a portion of the United States for 1–2 yr, then retrieved and redeployed farther east. Surface pressure observations at a sampling frequency of 1 Hz were examined during the period 1 January 2010–28 February 2014 when the seismic array was transitioning from the central to eastern continental United States. Surface pressure time series at over 900 locations were bandpass filtered to examine pressure perturbations on three temporal scales: meso- (10 min–4 h), subsynoptic (4–30 h), and synoptic (30 h–5 days) scales. Case studies of strong pressure perturbations are analyzed using web tools developed to visualize and track tens of thousands of such events with respect to archived radar imagery and surface wind observations. Seasonal assessments of the bandpass-filtered variance and frequency of large-magnitude events are conducted to identify prominent areas of activity. Large-magnitude mesoscale pressure perturbations occurred most frequently during spring in the southern Great Plains and shifted northward during summer. Synoptic-scale pressure perturbations are strongest during winter in the northern states with maxima located near the East Coast associated with frequent synoptic development along the coastal storm track.

scholarly journals Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

2021 ◽  
Author(s):  
Aaron Spring ◽  
István Dunkl ◽  
Hongmei Li ◽  
Victor Brovkin ◽  
Tatiana Ilyina
Keyword(s):  
Carbon Cycle ◽  
Initial Conditions ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
State Variables ◽  
Predictive Skill ◽  
Perfect Model ◽  
Prediction Systems ◽  
Global Carbon

Abstract. State-of-the-art carbon cycle prediction systems are initialized from reconstruction simulations in which state variables of the climate system are assimilated. While currently only the physical state variables are assimilated, biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we define 50 years of a control simulation under pre-industrial CO2 levels as our target representing observations. We nudge variables from this target onto arbitrary initial conditions 150 years later mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. We investigate the tracking performance, i.e. bias, correlation and root-mean-square-error between the reconstruction and the target, when nudging an increasing set of atmospheric, oceanic and terrestrial variables with a focus on the global carbon cycle explaining variations in atmospheric CO2. We compare indirect versus direct carbon cycle reconstruction against a resampled threshold representing internal variability. Afterwards, we use these reconstructions to initialize ensembles to assess how well the target can be predicted after reconstruction. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations on a global and regional scale much better than the resampled threshold. While reproducing the large scale variations, nudging introduces systematic regional biases in the physical state variables, on which biogeochemical cycles react very sensitively. Global annual surface oceanic pCO2 initial conditions are indirectly reconstructed with an anomaly correlation coefficient (ACC) of 0.8 and debiased root mean square error (RMSE) of 0.3 ppm. Direct reconstruction slightly improves initial conditions in ACC by +0.1 and debiased RMSE by −0.1 ppm. Indirect reconstruction of global terrestrial carbon cycle initial conditions for vegetation carbon pools track the target by ACC of 0.5 and debiased RMSE of 0.5 PgC. Direct reconstruction brings negligible improvements for air-land CO2 flux. Global atmospheric CO2 is indirectly tracked by ACC of 0.8 and debiased RMSE of 0.4 ppm. Direct reconstruction of the marine and terrestrial carbon cycles improves ACC by 0.1 and debiased RMSE by −0.1 ppm. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvements in the global carbon cycle, because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction trivial, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are even stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

Empirical Localization Functions for Ensemble Kalman Filter Data Assimilation in Regions with and without Precipitation

2015 ◽  
Vol 143 (9) ◽  
pp. 3664-3679 ◽  
Author(s):  
Lili Lei ◽  
Jeffrey L. Anderson ◽  
Glen S. Romine
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Ensemble Kalman Filter ◽  
Simulation Experiment ◽  
State Variables ◽  
Improve Performance ◽  
Spatially Homogeneous ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Vertical Localization

Abstract For ensemble-based data assimilation, localization is used to limit the impact of observations on physically distant state variables to reduce spurious error correlations caused by limited ensemble size. Traditionally, the localization value applied is spatially homogeneous. Yet there are potentially larger errors and different covariance length scales in precipitation systems, and that may justify the use of different localization functions for precipitating and nonprecipitating regions. Here this is examined using empirical localization functions (ELFs). Using output from an ensemble observing system simulation experiment (OSSE), ELFs provide estimates of horizontal and vertical localization for different observation types in regions with and without precipitation. For temperature and u- and υ-wind observations, the ELFs for precipitating regions are shown to have smaller horizontal localization scales than for nonprecipitating regions. However, the ELFs for precipitating regions generally have larger vertical localization scales than for nonprecipitating regions. The ELFs are smoothed and then applied in three additional OSSEs. Spatially homogeneous ELFs are found to improve performance relative to a commonly used localization function with compact support. When different ELFs are applied in precipitating and nonprecipitating regions, performance is further improved, but varying ELFs by observation type was not found to be as important. Imbalance in initial states caused by use of different localization functions is diagnosed by the domain-averaged surface pressure tendency. Forecasts from analyses with ELFs have smaller surface pressure tendencies than the standard localization, indicating improved initial balance with ELFs.

The decadal climate variations simulated in a coupled data assimilation system using only surface pressure observations

2020 ◽  
Author(s):  
Xiaosong Yang ◽  
Thomas Delworth ◽  
Fanrong Zeng ◽  
William Cooke ◽  
Liping Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Decadal Prediction ◽  
Climate Variations ◽  
Decadal Climate ◽  
Data Assimilation System ◽  
Assimilation System

<p>Initializing climate models for decadal prediction is a major challenge, in part due to the lack of long-term subsurface ocean observations and the changing nature of observing systems. In order to overcome these limitations, we have developed a novel method for initializing a climate model for decadal prediction. Using GFDL’s next-generation prediction system, we developed a coupled ensemble data assimilation system, which assimilated only surface pressure observations, since the surface pressure measurements have been made since the late 1800s. Physically, by assimilating high-frequency surface pressure observations we constrain the model to experience a sequence of wind and storms, and thus surface fluxes, that is very similar to what is observed. The hypothesis is that by having the ocean component of the coupled model experience a very similar sequence of surface fluxes as observations, the ocean component of the coupled model will gradually reproduce the same variations as the observed system.</p><p>We assimilated the observed surface pressure station data used in the latest 20-century reanalysis. A coupled simulation during 1960 to 2016 has been completed. In this talk, we will review how well the observed decadal climate variations (e.g., PDO and AMO) can be reproduced solely from the surface pressure observations.  In addition, we will explore the multi-decadal variations of the Atlantic meridional overturning circulation (AMOC) and its connection with the North Atlantic sea surface temperature. The feasibility of using this method to initialize coupled climate models for realistic decadal predictions will be discussed in the talk.            </p>

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