Bias correction of ocean bottom temperature and salinity simulations from a regional circulation model using regression kriging

2021 ◽  
Author(s):  
Jui‐Han Chang ◽  
Deborah R. Hart ◽  
Daphne Munroe ◽  
Enrique Curchitser
Keyword(s):  
Bias Correction ◽  
Circulation Model ◽  
Ocean Bottom ◽  
Regression Kriging ◽  
Bottom Temperature ◽  
Regional Circulation

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

2021 ◽  
Vol 7 ◽  
Author(s):  
Juan Durazo ◽  
Eric J. Kostelich ◽  
Alex Mahalov
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Bias Correction ◽  
Geomagnetic Storms ◽  
Circulation Model ◽  
Storm Event ◽  
Electron Content ◽  
Systematic Bias ◽  
Total Electron ◽  
Mean Square Errors

The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational FormoSat-3/COSMIC satellite observing platforms during a moderate (G2, Kp = 6) geomagnetic storm event on September 26–27, 2011. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. Two representative processes for the time evolution of the bias in the TIEGCM are tested: one in which the bias is constant and another in which the bias has an exponential growth and decay phase in response to strong geomagnetic forcing. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-h forecasts of total electron content (a key ionospheric variable) by 20–45%, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (∼1-h) of ionospheric dynamics during geomagnetic storms.

A multi-model assessment of sub-monthly polar motion and the associated ocean bottom pressure variability

2020 ◽  
Author(s):  
Michael Schindelegger ◽  
Alexander Harker ◽  
David Salstein ◽  
Henryk Dobslaw
Keyword(s):  
General Circulation ◽  
Polar Motion ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Ocean Bottom ◽  
Model Assessment ◽  
Bottom Pressure ◽  
Max Planck ◽  
Ocean Bottom Pressure ◽  
Partial Representation

<p>Budgeting geophysical fluid excitations against space-geodetic observations of polar motion reveals non-negligible residuals on sub-monthly time scales, typically 1−2 cm when projected onto the Earth's surface. A possible source for these discrepancies are imperfections in the hydrodynamic models used to derive the required ocean excitation functions. To guide future model improvements, we present a systematic assessment of the oceanic component of sub-monthly polar motion based on three global time-stepping models which are forced by the same atmospheric data but considerably differ in their numerical setup and physical parameterizations. In particular, we use ocean bottom pressure output and angular momenta from (i) the finite-element 2 Dimensions Gravity Wave Model (Mog2D), (ii) the baroclinic Max-Planck-Institute Ocean Model (MPIOM) at 1° horizontal resolution, representing the current industry standard, and (iii) a more experimental, eddy-permitting setup of the MITgcm (MIT General Circulation Model). Validations of data from 2007 to 2008 are performed against observed polar motion and daily GRACE (Gravity Recovery and Climate Experiment) solutions, which resolve the broad scales of ocean bottom pressure variability relevant for angular momentum considerations. No definite quantitative results are available at the time of this writing, but a specific question we aim to answer is whether the MITgcm run outperforms the other models in our validations, given its higher resolution and partial representation of flow interactions with major topographic features.</p>

Towards AOD1B RL07

2020 ◽  
Author(s):  
Linus Shihora ◽  
Henryk Dobslaw
Keyword(s):  
General Circulation ◽  
A Priori ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Temporal Variations ◽  
Ocean Dynamics ◽  
Global Ocean ◽  
Ocean Bottom ◽  
Data Set ◽  
Priori Information

<p>The Atmosphere and Ocean De-Aliasing Level-1B (AOD1B) product provides a priori information about temporal variations in the Earth's gravity field caused by global mass variability in the atmosphere and ocean and is routinely used as background model in satellite gravimetry. The current version 06 provides Stokes coefficients expanded up to d/o 180 every 3 hours. It is based on ERA-Interim and the ECMWF operational model for the atmosphere, and simulations with the global ocean general circulation model MPIOM consistently forced with the fields from the same atmospheric data-set.</p> <p>We here present preliminary numerical experiments in the development towards a new release 07 of AOD1B. The experiments are performed with the TP10 configuration of MPIOM and include (I) new hourly atmospheric forcing based on the new ERA-5 reanalysis from ECMWF; (II) an improved bathymetry around Antarctica including cavities under the ice shelves; and (III) an explicit implementation of the feedback effects of self-attraction and loading to ocean dynamics. The simulated ocean bottom pressure variability is discussed with respect to AOD1B version 6 as well as in situ ocean observations. A preliminary timeseries of hourly AOD1B-like coefficients for the year 2019 that incorporate the above mentioned improvements will be made available for testing purposes.</p>

scholarly journals On the bias correction of general circulation model output for Indian summer monsoon

2012 ◽  
Vol 20 (3) ◽  
pp. 349-356 ◽  
Author(s):  
Nachiketa Acharya ◽  
Surajit Chattopadhyay ◽  
U. C. Mohanty ◽  
S. K. Dash ◽  
L. N. Sahoo
Keyword(s):  
General Circulation Model ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Model Output ◽  
General Circulation Model Output

scholarly journals Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

2007 ◽  
Vol 34 ◽  
pp. 211-222 ◽  
Author(s):  
GA Baigorria ◽  
JW Jones ◽  
D Shin ◽  
A Mishra ◽  
JJ O’Brien
Keyword(s):  
Circulation Model ◽  
Crop Model ◽  
Model Simulations ◽  
Regional Circulation

scholarly journals Implications of ocean bottom temperatures on the catch ability of American lobster

2019 ◽  
Vol 79 ◽  
pp. 03007
Author(s):  
Xiaoxu Zhao ◽  
Pengwen Ding ◽  
Jilei Pang
Keyword(s):  
New England ◽  
Gulf Of Maine ◽  
Recent Decade ◽  
Temporal Variations ◽  
Effect Of Temperature ◽  
American Lobster ◽  
Ocean Bottom ◽  
Bottom Temperature ◽  
Using Data ◽  
The Impact

Since the beginning of the satellite era, the general trend of global and regional sea-surface temperature (SST) have continued to rise and, in the recent decade, the rate of warming has increased dramatically in the Gulf of Maine. However, due to variations in thermal stratification in the water column, SST is not the best measure to determine the impact on benthic organisms. So understanding the spatial and temporal variations of the ocean bottom temperature is critical to fisheries management. Since 2001, the Environmental Monitors on Lobster Traps (eMOLT) project has been implemented. The lobster fishermen have volunteered to collect bottom temperature and American lobster catch data from dozens of locations off the New England coast. Now we can use these data to analyze the relationship between ocean bottom temperature and lobster catch. Using data collected over the past decade, we examine the effect of temperature, temperature change, soak time and other factors on the catchability of lobsters. Our results suggest that there is a increase in catchability at the same time there is a) a temperature rise over many years and b) day-to-day temperature changes.

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