Precipitation simulated using the Regional Spectral Model (RSM) during the Coordinated Enhanced Observing Period (CEOP; July 1, 2001 to December 31, 2004) is evaluated by transferring the RSM to seven different regions of the globe and comparing the simulations with observations. These regions cover the eight Continental-Scale Experiments (CSEs) of the Global Energy and Water-cycle EXperiment (GEWEX) and encompass a broad variety of physical and dynamical meteorological processes. Gridded observations of the Global Precipitation Climatology Project (GPCP) and the Global Precipitation Climatology Center (GPCC), as well as CEOP reference site precipitation observations are compared with the RSM simulated precipitation for the first half of the CEOP Enhanced Observation Period (EOP) III (October 2002 to March 2003). After estimating the uncertainty ranges of both the model and the observations, model deficiencies were obtained for almost all model domains in terms of the amount of simulated precipitation. Although the RSM is able to accurately simulate the seasonal evolution and spatial distribution of precipitation, the RSM has an almost uniform positive bias (i.e., RSM values are greater than observed values) over almost all the domains. Most of the positive bias is associated with convection in the Intertropical Convergence Zone (ITCZ) or monsoonal convection in Southeast Asia. Predicted stratiform precipitation is also excessive over areas ofelevated topography. As the control simulations used a Relaxed Arakawa-Schubert scheme (RAS), sensitivity tests with three additional convection schemes were then carried out to assess whether the simulations could be improved. The additional convection schemes were: 1) the Simplified Arakawa-Schubert scheme (SAS); 2) the Kain-Fritsch scheme (KF); and 3) the National Centers for Atmospheric Research (NCAR) Community Climate Model (CCM) scheme. The precipitation simulation was significantly improved for almost all domains when using either the KF scheme or the SAS scheme. The best simulations of ITCZ convective precipitation and Southeast Asian monsoon convective precipitation were achieved using the SAS convection scheme.

Evaluation of RSM-Simulated Precipitation During CEOP

We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latent variables that are inferred using stochastic variational inference. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive likelihood governed with a Markovian prior, which is able to capture the non-linear inter-dependencies among weights to characterize multimodal temporal dynamics. This results in a flexible hierarchical deep generative factor analysis model that can be extended to (i) provide a collection of potentially interpretable states abstracted from the process dynamics, and (ii) perform short- and long-term vector time series prediction in a complex multi-relational setting. Our extensive experiments, which include simulated data and real data from a wide range of applications such as climate change, weather forecasting, traffic, infectious disease spread and nonlinear physical systems attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.

pdf/deep-switching-auto-regressive-factorization-application-to-37tp6wurah.pdf

Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting

For a parameterized hyperbolic system $\frac{du}{dt}=f(u,s)$ the derivative of the ergodic average $\langle J \rangle = \lim_{T \to \infty}\frac{1}{T}\int_0^T J(u(t),s)$ to the parameter $s$ can be computed via the Least Squares Shadowing algorithm (LSS). We assume that the sytem is ergodic which means that $\langle J \rangle$ depends only on $s$ (not on the initial condition of the hyperbolic system). After discretizing this continuous system using a fixed timestep, the algorithm solves a constrained least squares problem and, from the solution to this problem, computes the desired derivative $\frac{d\langle J \rangle}{ds}$. The purpose of this paper is to prove that the value given by the LSS algorithm approaches the exact derivative when the discretization timestep goes to $0$ and the timespan used to formulate the least squares problem grows to infinity.

Least Squares Shadowing method for sensitivity analysis of differential equations

High-resolution ensemble forecasts of a polar low by non-hydrostatic downscaling

Policy makers often have to deal with extreme events. We consider the two main approaches for the analysis of these events, statistical and physical statistics, which are usually considered dichotomically. We quickly delineate both limits and advantages of their tools and some new tentatives for overcoming the historical limits of the latter kind of approach. We highlight that for dealing adequately with extreme events it seems necessary to make use of a new kind of Decision Theory and new tools.

The Analysis of Extreme Events - Some Forecasting Approaches ∗

The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly, and affect predictability. Furthermore, predictability is limited by model errors due to the approximate simulation of atmospheric processes of the stateof-the-art numerical models. These two sources of uncertainties limit the skill of single, deterministic forecasts in an unpredictable way, with days of high/ poor quality forecasts randomly followed by days of high/poor quality forecasts. Two of the most recent advances in numerical weather prediction, the operational implementation of ensemble prediction systems and the development of objective procedures to target adaptive observations are discussed. Ensemble prediction is a feasible method to integrate a single, deterministic forecast with an estimate of the probability distribution function of forecast states. In particular, ensemble can provide forecasters with an objective way to predict the skill of single deterministic forecasts, or, in other words, to forecast the forecast skill. The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), based on the notion that initial condition uncertainties are the dominant source of forecast error, is described. Adaptive observations targeted in sensitive regions can reduce the initial conditions’uncertainties, and thus decrease forecast errors. More generally, singular vectors that identify unstable regions of the atmospheric flow can be used to identify optimal ways to adapt the atmospheric observing system.

Chaos and weather prediction January 2000

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Roberto Buizza

Author Tools

Chat about Author

Papers

Chaos and weather prediction January 2000