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Richard A. Craig
and
Wayne S. Hering

Abstract

The stratospheric warming of January-February 1957 is discussed with reference to the temperature and circulation changes which occurred. We attempt to present a concise description, rather than an explanation, of the observed sequence of events, beginning with 10-mb temperature rises in mid-January and ending with extensive circulation changes in February.

Full access
Olivia Martius
,
A. Hering
,
M. Kunz
,
A. Manzato
,
S. Mohr
,
L. Nisi
, and
S. Trefalt
Full access
Eric Gilleland
,
Amanda S. Hering
,
Tressa L. Fowler
, and
Barbara G. Brown

Abstract

Which of two competing continuous forecasts is better? This question is often asked in forecast verification, as well as climate model evaluation. Traditional statistical tests seem to be well suited to the task of providing an answer. However, most such tests do not account for some of the special underlying circumstances that are prevalent in this domain. For example, model output is seldom independent in time, and the models being compared are geared to predicting the same state of the atmosphere, and thus they could be contemporaneously correlated with each other. These types of violations of the assumptions of independence required for most statistical tests can greatly impact the accuracy and power of these tests. Here, this effect is examined on simulated series for many common testing procedures, including two-sample and paired t and normal approximation z tests, the z test with a first-order variance inflation factor applied, and the newer Hering–Genton (HG) test, as well as several bootstrap methods. While it is known how most of these tests will behave in the face of temporal dependence, it is less clear how contemporaneous correlation will affect them. Moreover, it is worthwhile knowing just how badly the tests can fail so that if they are applied, reasonable conclusions can be drawn. It is found that the HG test is the most robust to both temporal dependence and contemporaneous correlation, as well as the specific type and strength of temporal dependence. Bootstrap procedures that account for temporal dependence stand up well to contemporaneous correlation and temporal dependence, but require large sample sizes to be accurate.

Open access
Mathias W. Rotach
,
Paolo Ambrosetti
,
Felix Ament
,
Christof Appenzeller
,
Marco Arpagaus
,
Hans-Stefan Bauer
,
Andreas Behrendt
,
François Bouttier
,
Andrea Buzzi
,
Matteo Corazza
,
Silvio Davolio
,
Michael Denhard
,
Manfred Dorninger
,
Lionel Fontannaz
,
Jacqueline Frick
,
Felix Fundel
,
Urs Germann
,
Theresa Gorgas
,
Christoph Hegg
,
Alessandro Hering
,
Christian Keil
,
Mark A. Liniger
,
Chiara Marsigli
,
Ron McTaggart-Cowan
,
Andrea Montaini
,
Ken Mylne
,
Roberto Ranzi
,
Evelyne Richard
,
Andrea Rossa
,
Daniel Santos-Muñoz
,
Christoph Schär
,
Yann Seity
,
Michael Staudinger
,
Marco Stoll
,
Hans Volkert
,
Andre Walser
,
Yong Wang
,
Johannes Werhahn
,
Volker Wulfmeyer
, and
Massimiliano Zappa

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

Full access
Mathias W. Rotach
,
Paolo Ambrosetti
,
Christof Appenzeller
,
Marco Arpagaus
,
Lionel Fontannaz
,
Felix Fundel
,
Urs Germann
,
Alessandro Hering
,
Mark A. Liniger
,
Marco Stoll
,
Andre Walser
,
Felix Ament
,
Hans-Stefan Bauer
,
Andreas Behrendt
,
Volker Wulfmeyer
,
François Bouttier
,
Yann Seity
,
Andrea Buzzi
,
Silvio Davolio
,
Matteo Corazza
,
Michael Denhard
,
Manfred Dorninger
,
Theresa Gorgas
,
Jacqueline Frick
,
Christoph Hegg
,
Massimiliano Zappa
,
Christian Keil
,
Hans Volkert
,
Chiara Marsigli
,
Andrea Montaini
,
Ron McTaggart-Cowan
,
Ken Mylne
,
Roberto Ranzi
,
Evelyne Richard
,
Andrea Rossa
,
Daniel Santos-Muñoz
,
Christoph Schär
,
Michael Staudinger
,
Yong Wang
, and
Johannes Werhahn

Abstract

No Abstract available.

Full access