Download Studies in the Atmospheric Sciences by Mark L. Berliner, Douglas Nychka, Timothy Hoar PDF

By Mark L. Berliner, Douglas Nychka, Timothy Hoar

The nationwide heart for Atmospheric examine (NCAR) in Boulder, Colorado, is a big establishment appearing learn within the environmental sciences. After an open pageant, the nationwide technological know-how origin gave its monetary sup­ port to NCAR for the institution of a much-needed statistics/probability software within the geophysical sciences. the result's the NCAR Geophysical records venture (GSP). the first project of this application is: • To foster excellence within the use of contemporary statistical technology within the geo­ actual and environmental sciences. • to draw and help statistical scientists for collaborative study. to meet its venture, GSP engages in study in statistical technological know-how and its software to the atmospheric and allied sciences. It helps numerous collaborative efforts among statistical scientists and disciplinary scientists. Senior statisticians, either individuals of and viewers to GSP, paintings in conjunc­ tion with researchers at NCAR and somewhere else within the program of present statistical equipment in addition to in. the advance of latest statistical types and strategies. Senior GSP contributors and NCAR researchers additionally collaborate within the mentoring of junior participants, commonly post-doctoral point or information graduate students.

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2 Modeling Temperature Fields Another important bivariate field encountered in atmospheric science applications is temperature. Typically, daily or monthly temperature is summarized by its minimum and maximum, say Y(s) and Z{s). Unlike the previous example involving wind fields, there is not an obvious conditional model specification relating one of these variables to the other (except that Z{s) > Y(s)). , the two processes remain correlated, even conditional, on these covariates. Thus, if one's interest is in joint prediction of both fields, cokriging might seem the preferred method.

300 200 ........ •... "" "'" ... "" ... ~ ~ I ~ ~ :: ... 'OIl ,.. .. r::,··. :. ~..... ,...... ... , ' • " ,OIl " I , ... ~ . /,''' ' '' ', :• •• . : . - :"1 :. ::" .... ,. : .. ~ ' .. ··· · ·· ',' . '" ... FIGURE 5. Correlations of January range (top panel) and mean (middle panel). Cross-correlations are shown in the bottom panel. As an alternative modeling strategy, one might consider directly modeling a common latent process in a hierarchical model, perhaps with the latent process depending on other physical variables.

The matrices are functions of the 2 x 1 parameter vectors f3u and f3v, respectively and this parameterization is linear in f3 as discussed in Section 4. In the analysis of [Roy99c], the model was fit using Markov chain Monte Carlo (MCMC) techniques. The posterior mean wind and pressure fields for a particular time are depicted in Figure 2. ~ «i -l I f , I ... :-.. . -<-_ 305 I ? --310 t-- 315 320 Longitude FIGURE 2. Posterior mean wind and pressure fields. These results were particularly interesting because the posterior mean pressure field, estimated with no pressure data, resembled the European Center for Medium-Range Weather Forecasting's best guess at the true pressure field.

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