Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: James P. Hughes x
  • Refine by Access: All Content x
Clear All Modify Search
James P. Hughes and Peter Guttorp

Abstract

Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional on the current atmospheric pattern (termed a “weather state”), rain gauge stations in a network could be considered spatially independent. For a spatially dense network, this assumption is not tenable. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the conditional probability of rainfall given the weather state. Methods for fitting the parameters, assessing the goodness of fit of the model, and generating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washington State.

Full access
Eduardo Zorita, James P. Hughes, Dennis P. Lettemaier, and Hans von Storch

Abstract

Two statistical approaches for linking large-scale atmospheric circulation patterns and daily local rainfall are applied to GCM (general circulation model) climate simulations. The ultimate objective is to simulate local precipitation associated with altered climate regimes. Two regions, one in the Pacific-American sector (western region) and one in the American-Mid-Atlantic sector (eastern region), are explored.

The first method is based on Classification and Regression Trees (CART) analysis. The CART method classifies observed daily sea level pressure (SLP) fields into weather types that are most strongly associated with the presence/absence of rainfall at selected index stations. After applying this method to historical SLP observations, precipitation simulations associated with GCM SLP output were validated in terms of probability of occurrence and survival time of the weather states identified by the CART analysis. Daily rainfall time series were then generated from weather classes derived by application of CART to both daily SLP fields derived from historical observation and from GCM simulations. While the mean rainfall and probability distributions were rather well replicated, the precipitation generator based on this version of the CART technique had two important deficiencies: the generated dry periods were too short, on average, and the identification of weather states may be not invariant under coordinate rotations.

The second rainfall generator is based on the analog method and uses information about the evolution of the SLP field from several previous days. It considers a pool of past observations for the circulation patterns closest to the target circulation. It is similar to the CART method and in certain aspects it performs better, although some downward bias in the simulated rainfall persistence was still present. Applying both methods to the output of a 2 × CO2 GCM simulation produced only small changes in simulated precipitation, which is due to the small sensitivity of this variable to greenhouse forcing. The selection characteristics of the analogs are similar for observations, a control run, and a 2 × CO2 run, indicating that analogs for possible altered climates can be found in the historical record.

Full access
Charles O. Stanier, R. Bradley Pierce, Maryam Abdi-Oskouei, Zachariah E. Adelman, Jay Al-Saadi, Hariprasad D. Alwe, Timothy H. Bertram, Gregory R. Carmichael, Megan B. Christiansen, Patricia A. Cleary, Alan C. Czarnetzki, Angela F. Dickens, Marta A. Fuoco, Dagen D. Hughes, Joseph P. Hupy, Scott J. Janz, Laura M. Judd, Donna Kenski, Matthew G. Kowalewski, Russell W. Long, Dylan B. Millet, Gordon Novak, Behrooz Roozitalab, Stephanie L. Shaw, Elizabeth A. Stone, James Szykman, Lukas Valin, Michael Vermeuel, Timothy J. Wagner, Andrew R. Whitehill, and David J. Williams

Abstract

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

Full access
Charles O. Stanier, R. Bradley Pierce, Maryam Abdi-Oskouei, Zachariah E. Adelman, Jay Al-Saadi, Hariprasad D. Alwe, Timothy H. Bertram, Gregory R. Carmichael, Megan B. Christiansen, Patricia A. Cleary, Alan C. Czarnetzki, Angela F. Dickens, Marta A. Fuoco, Dagen D. Hughes, Joseph P. Hupy, Scott J. Janz, Laura M. Judd, Donna Kenski, Matthew G. Kowalewski, Russell W. Long, Dylan B. Millet, Gordon Novak, Behrooz Roozitalab, Stephanie L. Shaw, Elizabeth A. Stone, James Szykman, Lukas Valin, Michael Vermeuel, Timothy J. Wagner, Andrew R. Whitehill, and David J. Williams

Abstract

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multi-agency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes, the role of lake breezes, contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management, and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

Full access