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Paula J. Brown and Arthur T. DeGaetano

Abstract

Hourly dewpoint temperature data for the 1951–2006 period at 10 stations in the contiguous United States were investigated to determine if inhomogeneities in their records could be detected. At least three instrument changes are known to have occurred during this time period. The relatively sparse network of stations with dewpoint temperature data in the United States necessitated a nonconventional method to create a reference series. Utilizing nighttime occurrences of fog, clear/calm conditions, and precipitation as meteorological situations during which dewpoint temperatures and minimum temperatures are similar, three potential reference series based on daily minimum temperature were developed to test for inhomogeneities. Four stations with independent network neighbors recording hourly dewpoint data provided a direct validation of the effect of inhomogeneities on dewpoint temperatures. It was determined that fog conditions and the combined results from all three meteorologically based tests performed best when detecting documented inhomogeneities. However, a larger number of undocumented inhomogeneities, a feature common in most traditional inhomogeneity tests, were also detected that may or may not be valid.

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Paula J. Brown and Arthur T. DeGaetano

Abstract

U.S. hourly surface observations are examined at 145 stations to identify annual and seasonal changes in temperature, dewpoint, relative humidity, and specific humidity since 1930. Because of numerous systematic instrument changes that have occurred, a homogeneity assessment was performed on temperatures and dewpoints. Dewpoints contained higher breakpoint detection rates associated with instrumentation changes than did temperatures. Temperature trends were tempered by adjusting the data, whereas dewpoints were unaffected. The effects were the same whether the adjustments were based on statistically detected or fixed-year breakpoints. Average long-term trends (1930–2010) indicate that temperature has warmed but that little change has occurred in dewpoint and specific humidity. Warming is strongest in spring. There is evidence of inhomogeneity in the relative humidity record that primarily affects data from prior to 1950. Therefore, long-term decreases in relative humidity, which are strongest in winter, need to be viewed with caution. Trends since 1947 indicate that the warming of temperatures has coincided with increases in dewpoints and a moistening of specific humidity. This moistening is especially pronounced during the summer in the Midwest. For the nation, trends in relative humidity show little change for the period 1947–2010, during which these data are more homogeneous. Moistening has occurred throughout the central United States while other regions have experienced drying. Urban-related warming and drying trends are present in the data, but their effect is minimal. Regional changes in land use and moisture availability are likely influencing trends in atmospheric moisture.

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Paula J. Brown and Christian D. Kummerow

Abstract

Balancing global moisture budgets is a difficult task that is even more challenging at regional scales. Atmospheric water budget components are investigated within five tropical (15°S–15°N) ocean regions, including the Indian Ocean, three Pacific regions, and one Atlantic region, to determine how well data products balance these budgets. Initially, a selection of independent observations and a reanalysis product are evaluated to determine overall closure, between 1998 and 2007. Satellite-based observations from SeaFlux evaporation and Global Precipitation Climatology Project (GPCP) precipitation, together with Interim ECMWF Re-Analysis (ERA-Interim) data products, were chosen. Freshwater flux (evaporation minus precipitation) observations and reanalysis atmospheric moisture divergence regional averages are assessed for closure. Moisture budgets show the best closure over the Indian Ocean with a correlation of 89% and an overall imbalance of −3.0% of the anomalies. Of the five regions, the western Pacific Ocean region produces the worst atmospheric moisture budget closure of −21.1%, despite a high correlation of 93%. Average closure over the five regions is within 8.1%, and anomalies are correlated at 83%. ERA-Interim and Modern-Era Retrospective Analysis for Research and Applications (MERRA) evaporation rates are 29 and 19 mm month−1 greater than SeaFlux, respectively. To diagnose the differences, wind speed and humidity gradients of the three products are compared utilizing the bulk formula for evaporation. SeaFlux wind speeds are higher, but sea–air humidity gradients are lower. Higher humidity gradients in the reanalyses are due to much dryer near-surface air in ERA-Interim, and the same to a lesser degree in MERRA. These differences counteract each other somewhat, but overall humidity biases exceed wind biases. This is consistent with buoy observations.

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Paula J. Brown and Arthur T. DeGaetano

Abstract

Most climatological datasets are beset with urban temperature influences that distort long-term trends. Using an hourly dataset of 41 urban and rural stations from the United States, discriminant functions were developed using diurnal temperature range indices based on temperature, dewpoint, and dewpoint depression that capture the differences between the two environments. Based on data for 1997–2006, diurnal temperature range and nighttime dewpoint depression range indices provide the best classification variables to statistically discriminate between urban and rural climates. Of the 41 stations, 93% were correctly classified by this technique in a cross-validation analysis. An additional discriminant analysis specific to coastal stations was needed because coastal climates were noted to be aberrant. Here, all stations tested were correctly classified by the procedure. Temporal trends in discriminant scores indicate periods of time during which urbanization was occurring or increasing. Instrumental and location changes were noted to affect both temperature and dewpoint series and therefore the classification. However, such discontinuities can potentially be adjusted and the homogenized data used with the classification technique. The use of this data-driven approach complements existing methods used to classify the urban character of stations, because it is objective, is applicable in the presatellite era, and can infer changes at higher temporal resolution.

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Paula J. Brown, Raymond S. Bradley, and Frank T. Keimig

Abstract

The northeastern United States is one of the most variable climates in the world, and how climate extremes are changing is critical to populations, industries, and the environment in this region. A long-term (1870–2005) temperature and precipitation dataset was compiled for the northeastern United States to assess how the climate has changed. Adjustments were made to daily temperatures to account for changes in mean, variance, and skewness resulting from inhomogeneities, but precipitation data were not adjusted. Trends in 17 temperature and 10 precipitation indices at 40 stations were evaluated over three time periods—1893–2005, 1893–1950, and 1951–2005—and over 1870–2005 for a subset of longer-term stations. Temperature indices indicate strong warming with increases in the frequency of warm events (e.g., warm nights and warm summer days) and decreases in the frequency of cold events (e.g., ice days, frost days, and the cold spell duration indicator). The strongest warming is exhibited in the decrease in frost days and the increase in growing season length. Although maximum temperatures indices showed strong warming trends over the period 1893–1950, subsequent trends show little change and cooling. Few significant trends were present in the precipitation indices; however, they displayed a tendency toward wetter conditions. A stepwise multiple linear regression analysis indicated that some of the variability in the 27 indices from 1951 to 2002 was explained by the North Atlantic Oscillation, Pacific decadal oscillation, and Pacific–North American pattern. However, teleconnection patterns showed little influence on the 27 indices over a 103-yr period.

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Paula J. Brown, Christian D. Kummerow, and David L. Randel

Abstract

The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.

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Ruanyu Zhang, Christian D. Kummerow, David L. Randel, Paula J. Brown, Wesley Berg, and Zhenzhan Wang

Abstract

This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

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