A stochastic-dynamic model for global atmospheric mass field statistics

A model that yields the spatial correlation structure of atmospheric mass field forecast errors was developed. The model is governed by the potential vorticity equation forced by random noise. Expansion in spherical harmonics and correlation function was computed analytically using the expansion coefficients. The finite difference equivalent was solved using a fast Poisson solver and the correlation function was computed using stratified sampling of the individual realization of F(omega) and hence of phi(omega). A higher order equation for gamma was derived and solved directly in finite differences by two successive applications of the fast Poisson solver. The methods were compared for accuracy and efficiency and the third method was chosen as clearly superior. The results agree well with the latitude dependence of observed atmospheric correlation data. The value of the parameter c sub o which gives the best fit to the data is close to the value expected from dynamical considerations

The impact of scatterometer wind data on global weather forecasting

The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets

Statistics of locally coupled ocean and atmosphere intraseasonal anomalies in Reanalysis and AMIP data

International audienceWe apply a simple dynamical rule to determine the dominant forcing direction in locally coupled ocean-atmosphere anomalies in the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/ NCAR) reanalysis data. The rule takes into account the phase relationship between the low-level vorticity anomalies and the Sea Surface Temperature (SST) anomalies. Analysis of the frequency of persistent coupled anomalies for five-day average data shows that, in general, the ocean tends to force the atmosphere in the tropics while the atmosphere tends to force the ocean in the extratropics. The results agree well with those obtained independently using lagged correlations between atmospheric and oceanic variables, suggesting that the dynamical rule is generally valid. A similar procedure carried out using data from the NCEP global model run with prescribed SST (in which the coupling is one-way, with the ocean always forcing the atmosphere) produces fewer coupled anomalies in the extratropics. They indicate, not surprisingly, an increase in ocean-driving anomalies in the model. In addition, and very importantly, there is a strong reduction of persistent atmosphere-driving anomalies, indicating that the one-way interaction of the ocean in the model run may provide a spurious negative feedback that damps atmospheric anomalies faster than observed

Scaling properties of growing noninfinitesimal perturbations in space-time chaos

We study the spatiotemporal dynamics of random spatially distributed noninfinitesimal perturbations in one-dimensional chaotic extended systems. We find that an initial perturbation of finite size $\epsilon_0$ grows in time obeying the tangent space dynamic equations (Lyapunov vectors) up to a characteristic time $t_{\times}(\epsilon_0) \sim b - (1/\lambda_{max}) \ln (\epsilon_0)$, where $\lambda_{max}$ is the largest Lyapunov exponent and $b$ is a constant. For times $t < t_{\times}$ perturbations exhibit spatial correlations up to a typical distance $\xi \sim t^z$. For times larger than $t_{\times}$ finite perturbations are no longer described by tangent space equations, memory of spatial correlations is progressively destroyed and perturbations become spatiotemporal white noise. We are able to explain these results by mapping the problem to the Kardar-Parisi-Zhang universality class of surface growth.Comment: 4.5 pages LaTeX (RevTeX4) format, 3 eps figs included. Submitted to Phys Rev

Empirical correction of a toy climate model

Improving the accuracy of forecast models for physical systems such as the atmosphere is a crucial ongoing effort. Errors in state estimation for these often highly nonlinear systems has been the primary focus of recent research, but as that error has been successfully diminished, the role of model error in forecast uncertainty has duly increased. The present study is an investigation of a particular empirical correction procedure that is of special interest because it considers the model a "black box", and therefore can be applied widely with little modification. The procedure involves the comparison of short model forecasts with a reference "truth" system during a training period in order to calculate systematic (1) state-independent model bias and (2) state-dependent error patterns. An estimate of the likelihood of the latter error component is computed from the current state at every timestep of model integration. The effectiveness of this technique is explored in two experiments: (1) a perfect model scenario, in which models have the same structure and dynamics as the true system, differing only in parameter values; and (2) a more realistic scenario, in which models are structurally different (in dynamics, dimension, and parameterization) from the target system. In each case, the results suggest that the correction procedure is more effective for reducing error and prolonging forecast usefulness than parameter tuning. However, the cost of this increase in average forecast accuracy is the creation of substantial qualitative differences between the dynamics of the corrected model and the true system. A method to mitigate the structural damage caused by empirical correction and further increase forecast accuracy is presented.Comment: 16 pages, 12 figure

Data assimilation insights on selecting the most valuable atmospheric measurements

We discuss how objective guidance on selecting the most valuable atmospheric measurements on future Mars spacecraft missions can be provided through already developed Martian atmospheric data assimilation systems, and in particular through Observing System Simulation Experiments (OSSEs) which are widely used to design instruments for the Earth’s atmosphere

Predicting flow reversals in chaotic natural convection using data assimilation

A simplified model of natural convection, similar to the Lorenz (1963) system, is compared to computational fluid dynamics simulations in order to test data assimilation methods and better understand the dynamics of convection. The thermosyphon is represented by a long time flow simulation, which serves as a reference "truth". Forecasts are then made using the Lorenz-like model and synchronized to noisy and limited observations of the truth using data assimilation. The resulting analysis is observed to infer dynamics absent from the model when using short assimilation windows. Furthermore, chaotic flow reversal occurrence and residency times in each rotational state are forecast using analysis data. Flow reversals have been successfully forecast in the related Lorenz system, as part of a perfect model experiment, but never in the presence of significant model error or unobserved variables. Finally, we provide new details concerning the fluid dynamical processes present in the thermosyphon during these flow reversals

Potential and Actual impacts of deforestation and afforestation on land surface temperature

Forests are undergoing significant changes throughout the globe. These changes can modify water, energy, and carbon balance of the land surface, which can ultimately affect climate. We utilize satellite data to quantify the potential and actual impacts of forest change on land surface temperature (LST) from 2003 to 2013. The potential effect of forest change on temperature is calculated by the LST difference between forest and nearby nonforest land, whereas the actual impact on temperature is quantified by the LST trend difference between deforested (afforested) and nearby unchanged forest (nonforest land) over several years. The good agreement found between potential and actual impacts both at annual and seasonal levels indicates that forest change can have detectable impacts on surface temperature trends. That impact, however, is different for maximum and minimum temperatures. Overall, deforestation caused a significant warming up to 0.28 K/decade on average temperature trends in tropical regions, a cooling up to -0.55 K/decade in boreal regions, a weak impact in the northern temperate regions, and strong warming (up to 0.32 K/decade) in the southern temperate regions. Afforestation induced an opposite impact on temperature trends. The magnitude of the estimated temperature impacts depends on both the threshold and the data set (Moderate Resolution Imaging Spectroradiometer and Landsat) by which forest cover change is defined. Such a latitudinal pattern in temperature impact is mainly caused by the competing effects of albedo and evapotranspiration on temperature. The methodology developed here can be used to evaluate the temperature change induced by forest cover change around the globe.Maryland Council on the Environment [1357928]; National Natural Science Foundation of China [41371096, 41130534]; China Scholar Council fellowship [201306010169]; National Socio-Environmental Synthesis Center-NSF [DBI-1052875]SCI(E)[email protected]

Use of the breeding technique to estimate the structure of the analysis 'errors of the day'

A 3D-variational data assimilation scheme for a quasi-geostrophic channel model (Morss, 1998) is used to study the structure of the background error and its relationship to the corresponding bred vectors. The &quot;true&quot; evolution of the model atmosphere is defined by an integration of the model and &quot;rawinsonde observations&quot; are simulated by randomly perturbing the true state at fixed locations. Case studies using different observational densities are considered to compare the evolution of the Bred Vectors to the spatial structure of the background error. In addition, the bred vector dimension (BV-dimension), defined by Patil et al. (2001) is applied to the bred vectors. It is found that after 3-5 days the bred vectors develop well organized structures which are very similar for the two different norms (enstrophy and streamfunction) considered in this paper. When 10 surrogate bred vectors (corresponding to different days from that of the background error) are used to describe the local patterns of the background error, the explained variance is quite high, about 85-88%, indicating that the statistical average properties of the bred vectors represent well those of the background error. However, a subspace of 10 bred vectors corresponding to the time of the background error increased the percentage of explained variance to 96-98%, with the largest percentage when the background errors are large. These results suggest that a statistical basis of bred vectors collected over time can be used to create an effective constant background error covariance for data assimilation with 3D-Var. Including the &quot;errors of the day&quot; through the use of bred vectors corresponding to the background forecast time can bring an additional significant improvement

Protein tyrosine phosphatases: the problems of a growing family

Protein tyrosine phosphorylation is now recognized as an important component of the control of many fundamental aspects of cellular function, including growth and differentiation, cell cycle and cytoskeletal integrity. In vivo, the net level of phosphorylation of tyrosyl residues in a target substrate reflects the balance between the competing action of kinases and phosphatases. We are examining physiological roles for protein tyrosine phosphorylation, pursuing the problem from the perspective of the enzymes that catalyze the dephosphorylation reaction, the protein tyrosine phosphatases (PTPases). The PTPases have, until recently, been somewhat neglected relative to the protein tyrosine kinases (PTKs). However, considerable progress has been made in identifying new members of the PTPase family, and it appears that they constitute a novel class of signal transducing molecules that rival the PTKs in their structural diversity and complexity. One of the principal reasons that the study of PTPases has lagged behind that of the..