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Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for "average conditions," little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions' growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid. [ABSTRACT FROM AUTHOR]

Research on the interaction between the Madden–Julian oscillation (MJO) and rainfall around Jakarta is limited, although the influence of the MJO on increased rainfall is acknowledged as one of the primary causes of flooding in the region. This paper investigates the local rainfall response around Jakarta to the MJO. We used C-band Doppler radar in October–April during 2009–12 to study rain-rate characteristics at much higher resolution than previous analyses. Results show that the MJO strongly modulates rain rates over the region; however, its effect varies depending on topography. During active phases, MJO induces a high rain rate over the ocean and coast, meanwhile during suppressed phases, it generates a high rain rate mainly over the mountains. In phase 2 of the MJO we find the strongest increase in mean and extreme rain rate, which is earlier in the MJO cycle than most studies reported, based on lower-resolution data. This higher rain rate is likely due to increases in convective and stratiform activities. The MJO promotes more stratiform rain once it resides over Indonesia. In phase 5, over the northwestern coast and western part of the radar domain, the MJO might bring forward the peak of the hourly rain rate that occurs in the early morning. This is likely due to a strong westerly flow arising from MJO superimposed westerly monsoonal flow, blocked by the mountains, inducing a strong wind propagating offshore resulting in convection near the coast in the morning. Our study demonstrates the benefits of using high-resolution radar for capturing local responses to the larger-scale forcing of the MJO in Indonesia. Significance Statement: Rainfall in Jakarta and its surroundings is highly variable and often heavy resulting in devastating floods. In this region, in the wet season, rainfall is influenced by large-scale climate variability including the Madden–Julian oscillation (MJO) characterized by eastward propagation of clouds near the equatorial regions on intraseasonal time scales. The MJO has been known to increase the probability of rainfall occurrence and its magnitude, but we show that the impact differs in varying topography. The frequency and intensity of rainfall increase over land areas including mountains even when MJO has not arrived in Indonesia. Meanwhile, once MJO moves through Indonesia, the frequency and magnitude of the rainfall increases over the northern coast and ocean as well as in the west of the radar domain. [ABSTRACT FROM AUTHOR]

Monitoring precipitation forecast skill in global numerical weather prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products, on the other hand, provide near-global coverage at a resolution of ∼10–25 km, but limitations on data quality (e.g., biases) must be accommodated. In this paper the stable equitable error in probability space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology and then applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS are explored by comparing the gauge scores, based on extracting the nearest model grid point, with those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ∼6000 locations (dictated by the number of gauges available globally) with all land points within the TRMM region of 50°N and 50°S. The forecast performance over the oceanic areas is compared with performance over land. While the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

This paper reports a consistent seesaw relationship between interdecadal precipitation variability over North China and the Southwest United States, which can be found in observations and simulations with several models. Idealized model simulations suggest the seesaw could be mainly driven by the interdecadal Pacific oscillation (IPO), through a large-scale circulation anomaly occupying the entire northern North Pacific, while the Atlantic multidecadal oscillation (AMO) contributes oppositely and less. Modulation of precipitation by the IPO tends to be intensified when the AMO is in the opposite phase, but weakened when the AMO is in the same phase. The warm IPO phase is associated with an anomalous cyclone over the northern North Pacific; consequently, anomalous southwesterly winds bring more moisture and rainfall to the Southwest United States, while northwesterly wind anomalies prevail over North China with negative rainfall anomalies. The east–west seesaw of rainfall anomalies reverses sign when the circulation anomaly becomes anticyclonic during the cold IPO phase. The IPO-related tropical SST anomalies affect the meridional temperature gradient over the North Pacific and adjacent regions and the mean meridional circulation. In the northern North Pacific, the atmospheric response to IPO forcing imposes an equivalent barotropic structure throughout the troposphere. An important implication from this study is the potential predictability of drought-related water stresses over these arid and semiarid regions, with the progress of our understanding and prediction of the IPO and AMO. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

This paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the "east plus west" sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983–94, when station density was relatively high, and 1998–2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983–94 in the east plus west sector is on the order of 20 mm month−1 for GPCC V7 and 20–30 mm month−1 for the other products. The compares with 40–60 mm month−1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30–40 mm month−1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998–2010), when the station network is sparse, than during the period 1983–94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30–40 mm month−1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites. [ABSTRACT FROM AUTHOR]

This paper describes the assumptions, equations, and procedures of the RheaG weather generator algorithm (WGA). RheaG was conceived for the generation of robust daily meteorological time series, whether in static or transient climate conditions. Here we analyze its performance in four Iberian locations—Bilbao, Barcelona, Madrid, and Sevilla—with differentiated climate characteristics. To validate the RheaG WGA, we compared observed and generated meteorological time series' statistical properties of precipitation, maximum temperature, and minimum temperature for all four locations. We also compared observed and simulated rain events spell length probabilities in all four locations. Finally, RheaG includes two weather generation procedures: one in which monthly mean values for meteorological variables are unconstrained and one in which they are constrained according to a predefined baseline climate variability. Here, we compare the two weather generation procedures included in RheaG using the observed data from Barcelona. Our results present a high agreement in the statistical properties and the rain spell length probabilities between observed and generated meteorological time series. Our results show that RheaG accurately reproduces seasonal patterns of the observed meteorological time series for all four locations, and it is even able to differentiate two climatic seasons in Bilbao that are also present in the observed data. We find a trade-off between generation procedures in which the unconstrained procedure better reproduces the variability of monthly and yearly precipitation than the constrained one, but the constrained procedure is able to keep the same climatic signal across meteorological time series. Thus, the first procedure is more accurate, but the latter is able to maintain spatial autocorrelation among generated meteorological time series. [ABSTRACT FROM AUTHOR]

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS. [ABSTRACT FROM AUTHOR]

The Bay of Bengal (BoB) plays a fundamental role in controlling the weather systems that make up the South Asian summer monsoon system. In particular, the southern BoB has cooler sea surface temperatures (SST) that influence ocean–atmosphere interaction and impact the monsoon. Compared to the southeastern BoB, the southwestern BoB is cooler, more saline, receives much less rain, and is influenced by the summer monsoon current (SMC). To examine the impact of these features on the monsoon, the BoB Boundary Layer Experiment (BoBBLE) was jointly undertaken by India and the United Kingdom during June–July 2016. Physical and biogeochemical observations were made using a conductivity–temperature–depth (CTD) profiler, five ocean gliders, an Oceanscience Underway CTD (uCTD), a vertical microstructure profiler (VMP), two acoustic Doppler current profilers (ADCPs), Argo floats, drifting buoys, meteorological sensors, and upper-air radiosonde balloons. The observations were made along a zonal section at 8°N between 85.3° and 89°E with a 10-day time series at 8°N, 89°E. This paper presents the new observed features of the southern BoB from the BoBBLE field program, supported by satellite data. Key results from the BoBBLE field campaign show the Sri Lanka dome and the SMC in different stages of their seasonal evolution and two freshening events during which salinity decreased in the upper layer, leading to the formation of thick barrier layers. BoBBLE observations were taken during a suppressed phase of the intraseasonal oscillation; they captured in detail the warming of the ocean mixed layer and the preconditioning of the atmosphere to convection. [ABSTRACT FROM AUTHOR]

Precipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M- B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954-2013 to investigate regional trends in intermittency due to climate variability and global warming. No evidence is found of a global shift in intermittency but a weak trend toward burstier precipitation patterns and longer dry spells in the south of Europe (i.e., Portugal, Spain, and Italy) and an opposite trend toward steadier and more correlated precipitation patterns in Norway, Sweden, and Finland is observed. [ABSTRACT FROM AUTHOR]

In the last several decades, extended dry periods have affected the Mediterranean area with dramatic impacts on water resources. Climate models are predicting further warming, with negative effects on water availability. The authors analyze the hydroclimatic tendencies of a typical Mediterranean basin, the Flumendosa basin located in Sardinia, an island in the center of the Mediterranean Sea, where in the last 30 years a sequence of dry periods has seriously impacted the water management system. Interestingly, in the historic record the annual runoff reductions have been more pronounced than the annual precipitation reductions. This paper performs an analysis that links this runoff decrease to changes in the total annual precipitation and its seasonal structure. The seasonality is a key determinant of the surface runoff process, as it reflects the degree to which rainfall is concentrated during the winter. The observed reductions in winter precipitation are shown here to be well correlated (Pearson correlation coefficient of 20.5) with the North Atlantic Oscillation (NAO) index. Considering the predictability of the winter NAO, there is by extension an opportunity to predict future winter precipitation and runoff tendencies. The recent hydroclimatic trends are shown to impact hydrologic design criteria for water resources planning. The authors demonstrate that there is a dangerous increase of the drought severity viewed from the perspective of water resources planning. [ABSTRACT FROM AUTHOR]

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December-February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season's rainfall over the period 1500-2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño-Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance. [ABSTRACT FROM AUTHOR]

The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the 'forecast phase,' the forecasts from all of the member models use the downscaling coefficients of the 'training phase.' The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts. [ABSTRACT FROM AUTHOR]

A full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentieth-century remains elusive some 25 yr after the height of the event. Previous studies have suggested that this drying trend may be explained by either decadal modes of natural variability or by human-driven emissions (primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal variability and model formulation. The sizes of these ensembles enable the relative role of human-driven changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic aerosol changes are likely to explain most of the underlying 1940-80 drying signal and a notable proportion of the more pronounced 1950-80 drying. [ABSTRACT FROM AUTHOR]

This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals. [ABSTRACT FROM AUTHOR]

This paper examines the mean annual cycle of rainfall and general circulation features over West Africa and central Africa for 1958–97. Rainfall is examined using a 1400-station archive compiled by the first author. Other circulation features are examined using the NCEP–NCAR reanalysis dataset. Important features of the reanalysis zonal wind field are shown to compare well with the seasonal evolution described by the radiosonde observations. In addition to the well-known African easterly jet (AEJ) of the Northern Hemisphere, the seasonal evolution of its Southern Hemisphere counterpart is also described. Thermal wind calculations show that although the southern jet is weaker, its existence is also due to a local reversal of the surface temperature gradient. In the upper troposphere, a strong semiannual cycle is shown in the 200-mb easterlies and a feature like the tropical easterly jet (TEJ) is evident south of the equator in January and February. The paper describes the movement of the rainbelt between central and West Africa. An asymmetry in the northward and southward migration of the rainbelt is evident. The paper discusses the influence that the jets may have on rainfall and possible feedback effects of rainfall on the jets. Evidence suggests that the midtropospheric jets influence the development of the rainy season, but also that the rainfall affects the surface temperature gradient and in turn the jets. In the Northern Hemisphere, east of 20°E, the axis of the TEJ is located so that it may promote convection by increasing upper-level divergence. However, west of 10°E and in the Southern Hemisphere, the location of the TEJ is consistent with the suggestion that it is the equatorward outflow of convection that produces the TEJ. [ABSTRACT FROM AUTHOR]

Hawaii's high and steep topography leads to pronounced small-scale variations in climate, and this makes comprehensive modeling of the weather and climate particularly challenging. This paper describes a regional model formulation designed for simulations of the microclimates in Hawaii and then documents and analyzes an extended retrospective simulation for near-present-day conditions. Part II will apply the model to projected climate conditions near the end of the present century. A nested version of the Advanced Research version of the Weather Research and Forecasting Model with fine horizontal resolution and improved physics for the Hawaiian region has been configured. A 20-yr triply nested simulation of the atmospheric flow was undertaken with a 3-km-resolution mesh covering all main Hawaiian Islands and a 1-km mesh over Maui. Ocean surface temperatures are prescribed from observations, and meteorological fields at the boundaries of the outermost domain are taken from global reanalyses. The simulations are compared to surface, balloon, and satellite observations over the same period. The 3-km version of the model realistically simulates the frequency of trade wind inversions, time-mean rainfall, and other variables on relatively small scales over the island of Hawaii. There is a reasonable agreement between observed and simulated mean rainfall patterns over the other islands as well. However, the simulated distribution of mean rainfall over Kauai and (most particularly) Maui and Oahu reveals some significant deficiencies, which is attributed to inadequate resolution of the topography on these islands. The 1-km simulation over Maui shows clear improvement in the mean rainfall over the 3-km version. [ABSTRACT FROM AUTHOR]

Temporal and spatial distributions of the North American monsoon have been studied previously with rainfall and satellite data. In the current study, the monsoon is examined with lightning data from Vaisala's Global Lightning Dataset (GLD360). GLD360 has been operating for over three years and provides sufficient data to develop an exploratory climatology with minimal spatial variation in detection efficiency and location accuracy across the North American monsoon region. About 80% of strokes detected by GLD360 are cloud to ground. This paper focuses on seasonal, monthly, and diurnal features of lightning occurrence during the monsoon season from Mazatlán north-northwest to northern Arizona and New Mexico. The goal is to describe thunderstorm frequency with a dataset that provides uniform spatial coverage at a resolution of 2-5 km and uniform temporal coverage with individual lightning events resolved to the millisecond, compared with prior studies that used hourly point rainfall or satellite data with a resolution of several kilometers. The monthly lightning stroke density over northwestern Mexico increases between May and June, as thunderstorms begin over the high terrain east of the Gulf of California. The monthly lightning stroke density over the entire region increases dramatically to a maximum in July and August. The highest stroke densities observed in Mexico approach those observed by GLD360 in subtropical and tropical regions in Africa, Central and South America, and Southeast Asia. The diurnal cycle of lightning exhibits a maximum over the highest terrain near noon, associated with daytime solar heating, a maximum near midnight along the southern coast of the Gulf, and a gradual decay toward sunrise. [ABSTRACT FROM AUTHOR]

Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT's value as a complementary method of estimating rainfall through examples of successful operational application. [ABSTRACT FROM AUTHOR]

In this paper, statistical properties of rainfall are derived from 14 years of Tropical Rainfall Measuring Mission data to optimize the use of flight hours for the upcoming High Altitude Ice Crystals (HAIC)/High Ice Water Content (HIWC) program. This program aims to investigate the convective processes responsible for the generation of the high ice water content that has been recognized as a threat to civil aviation. The probability that convective cells are conducive to HIWC is also further investigated using three years of C-band polarimetric radar data. Further insights into the variability of convective rainfall and favorable conditions for HIWC are also gained using two different methods to characterize the large-scale atmospheric conditions around Darwin, Australia (the Madden-Julian oscillation and the Darwin atmospheric regimes), and the underlying surface type (oceanic vs continental). The main results from the climatology relevant to flight-plan decision making are (i) convective cells conducive to HIWC should be found close to Darwin, (ii) at least 90% of convective cells are conducive to HIWC at 10- and 12-km flight levels, (iii) multiple flights per day in favorable large-scale conditions will be needed so as to utilize the 150 project flight hours, (iv) the largest numbers of HIWC radar pixels are found around 0300 and 1500 local time, and (v) to fulfill the requirement to fly 90 h in oceanic convection and 60 h in or around continental convection, a minimum 'acceptable' size of the convective area has been derived and should serve as a guideline for flight-decision purposes. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) should be evaluated with respect to their ability to downscale large-scale climate information to the local scales, which are sometimes strongly modulated by surface conditions. This is the case for La Réunion (southwest Indian Ocean) because of its island context and its complex topography. Large-scale atmospheric configurations such as tropical cyclones (TCs) may have an amplifying effect on local rainfall patterns that only a very high-resolution RCM, forced by the large scales and resolving finescale processes, may simulate properly. This paper documents the capability of the Weather Research and Forecasting Model (WRF) RCM to regionalize rainfall variability at very high resolution (680 m) over La Réunion island for daily to seasonal time scales and year-to-year differences. Two contrasted wet seasons (November-April) are selected: 2000-01 (abnormally dry) and 2004-05 (abnormally wet). WRF rainfall is compared to a dense network of rain gauge records interpolated onto the WRF grid through the regression-kriging (RK) technique. RK avoids the point-to-grid comparison issue, but produces imperfect estimates due to sampling, so its quality also needs to be tested. Seasonal rainfall amounts and contrasts produced by WRF are fairly realistic. At intraseasonal and daily time scales, differences to RK are more sizable. These differences are not easy to interpret in sectors where the rain gauge network is less dense and the quality of RK more uncertain, as over the eastern slopes of Piton de la Fournaise volcano where WRF seems to simulate more realistic rainfall than RK. Finally, the heavy rainfall associated with TC Ando on 6 January 2001, is documented. WRF shows weak disagreements with RK, indicating its capability to regionalize rainfall during extreme events. [ABSTRACT FROM AUTHOR]

Recent advances in the collection and analysis of paleoclimate data have provided significant insights into preinstrumental environmental events and processes, enabling a greater understanding of long-term environmental change and associated hydroclimatic risks. Unfortunately, it is often the case that there is a dearth of readily available paleoclimate data from regions where such insights and long-term data are most needed. The Murray-Darling basin (MDB), known as Australia's 'food bowl,' is an example of such a region where currently there are very limited in situ paleoclimate data available. While previous studies have utilized paleoclimate proxy records of large-scale climate mechanisms to infer preinstrumental MDB hydroclimatic variability, there is a lack of studies that utilize Australian terrestrial proxy records to garner similar information. Given the immediate need for improved understanding of MDB hydroclimatic variability, this paper identifies key locations in Australia where existing and as yet unrealized paleoclimate records will be most useful in reconstructing such information. To identify these key locations, rainfall relationships between MDB and non-MDB locations were explored through correlations and principal component analysis. An objective analysis using optimal interpolation was then used to pinpoint the most strategic locations to further develop proxy records and gain insights into the benefits of obtaining this additional information. The findings reveal that there is potential for the future assembly of high-resolution paleoclimate records in Australia capable of informing MDB rainfall variability, in particular southeast Australia and central-northern Australia. This study highlights the need for further investment in the development of these potential proxy sources to subsequently enable improved assessments of long-term hydroclimatic risks. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates a satellite rainfall estimation technique that combines infrared and lightning information to estimate precipitation in deep convective systems. The algorithm is developed and tested using seven years (2002-08) of TRMM measurements over the southern United States during the warm season. Lightning information is coupled with a modified IR-based convective-stratiform technique (CST) and produces a lightning-enhanced CST (CSTL). Both the CST and CSTL are then applied to the training (2002-04) and independent (2005-08) datasets. In general, this study shows significant improvement over the IR rainfall estimates (rain area, intensity, and volume) by adding lightning information. The CST and CSTL display critical skill in estimating warm-season precipitation and the performance is very stable. The CST can generally identify the heavy (convective) and light rain regions, while CSTL further identifies convective areas that are missed by CST and removes convective cores that are incorrectly defined by CST. Specifically, the CSTL improves the convective cell detection by 5% and reduces the convective false alarm rate by more than 30%. Similarly, CSTL substantially improves the CST in the overall estimate of instantaneous rainfall rates. For example, when compared with passive microwave estimates, CSTL increases the correlation coefficient by 30%, reduces the bias by 50%, and reduces RMSE by 25%. Both CST and CSTL reproduce the rain area and volume fairly accurately over a region, although both techniques show some degree of overestimation relative to radar estimates. [ABSTRACT FROM AUTHOR]

The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean-Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts. [ABSTRACT FROM AUTHOR]

This paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983-2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems. [ABSTRACT FROM AUTHOR]

This paper examines the factors governing rainfall variability in western equatorial Africa (WEA) during the April-June rainy season. In three of the five regions examined some degree of large-scale forcing is indicated, particularly in the region along the Atlantic coast. Interannual variability in this coastal sector also demonstrates a strong link to changes in local sea surface temperatures (SSTs) and the South Atlantic subtropical high. To examine potential causal mechanisms, various atmospheric parameters are evaluated for wet and dry composites. The results suggest that the intensity of the zonal circulation in the global tropics is a crucial control on rainfall variability over WEA. A La Niña (El Niño)-like signal in both SSTs and zonal circulation over the Pacific is apparent in association with the wet (dry) conditions in the western sector. However, remote forcing from the Pacific modulates the circulation over Africa indirectly by way of synchronous changes in the entire Indian or Atlantic Ocean. Anomalies in the local zonal winds are similar in all three regions: the wet (dry) composite is associated with an intensification (weakening) of the upper-tropospheric easterlies and low-level westerlies, but a weakening (intensification) of the midlevel easterlies. This work also suggests that, in most cases, the relationship between local SSTs and rainfall reflects a common remote forcing by the large-scale atmosphere-ocean system. This forcing is manifested via changes in the zonal circulation. Thus, the statistical associations between rainfall and SSTs do not indicate direct forcing by local SSTs. One point of evidence for this conclusion is the stronger association with atmospheric parameters than with SSTs. [ABSTRACT FROM AUTHOR]

Studies carried out worldwide show that topography influences rainfall climatology. As in most western Mediterranean regions, the mountainous Cévennes-Vivarais area in France regularly experiences extreme precipitation that may lead to devastating flash floods. Global warming could further aggravate this situation, but this possibility cannot be confirmed without first improving the understanding of the role of topography in the regional climate and, in particular, for extreme rainfall events. This paper focuses on organized banded rainfall and evaluates its contribution to the rainfall climatology of this region. Stationary rainfall systems made up of such bands are triggered and enhanced by small-scale interactions between the atmospheric flow and the relief. Rainbands are associated with shallow convection and are also present in deep-convection events for specific flux directions. Such precipitation patterns are difficult to observe both with operational weather radar networks, which are not designed to observe low-level convection within complex terrain, and with rain gauge networks, for which gauge spacing is typically larger than the bandwidth. A weather class of banded orographic shallow-convection events is identified, and the contribution of such events to annual or seasonal precipitation over the region is assessed. Moreover, a method is also proposed to quantify the contribution of banded convection during specific deep-convection events. It is shown that even though these orographically driven banded precipitation events produce moderate precipitation intensities they have long durations and therefore represent a significant amount of the rainfall climatology of the region, producing up to 40%% of long-term total precipitation at certain locations. [ABSTRACT FROM AUTHOR]

Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time--space scales over the whole domain of the dataset. Here, ''uncertainty'' refers to the general concept of the ''deviation'' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20%% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer. [ABSTRACT FROM AUTHOR]

Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings. [ABSTRACT FROM AUTHOR]

Thunderstorms frequently produce brief flooding or minor damage, though far fewer lead to major flooding and widespread or significant damage. Outbreaks of such storms exact large tolls on their victims and can compromise, or completely overwhelm, the emergency response infrastructure. This paper derives empirical frequencies and recurrence intervals of “high end” convective weather events in the Minneapolis–St. Paul, Minnesota, metropolitan area from archived tornado, hail, damaging-wind, and high-density daily rainfall data, as well as historical records and accounts. Two classes of high-impact events are analyzed: those with the potential to produce widespread damage or disruption and those virtually certain to do so. Storms in this first class recur within the area, on average, 3 times per year, while the more extreme storms recur every 2–2.5 yr on average. Owing to well-established spatial and temporal inhomogeneities in observed severe weather data, true recurrence intervals are probably somewhat shorter. In the context of ongoing regional population growth, the area is becoming increasingly vulnerable to major damage and potential casualties from these major storm events. [ABSTRACT FROM AUTHOR]

Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products. [ABSTRACT FROM AUTHOR]

The dynamical core of the Regional Atmospheric Modeling System has been tailored to simulate the infrasound of vortex motions and diabatic cloud processes in a convective storm. Earlier studies have shown that the customized model (c-RAMS) adequately simulates the infrasonic emissions of generic vortex oscillations. This paper provides evidence that c-RAMS accurately simulates the infrasound associated with parameterized phase transitions of cloud moisture. Specifically, analytical expressions are derived for the infrasonic emissions of evaporating water droplets in dry and humid environments. The dry analysis considers two single-moment parameterizations of the microphysics, which have distinguishable acoustic signatures. In general, the analytical results agree with the numerical output of the model. An appendix briefly demonstrates the ability of c-RAMS to accurately simulate the infrasound of the entropy and mass sources generated by an equilibrating cloud of icy hydrometeors. [ABSTRACT FROM AUTHOR]

A simple model is developed that predicts climatological rainfall, vertical motion, and diabatic heating profiles over the tropical oceans given the sea surface temperature (SST), using statistical relationships deduced from the 40-yr ECMWF Re-Analysis (ERA-40). The model allows for two modes of variability in the vertical motion profiles: a shallow mode responsible for all “boundary layer” convergence between 850 hPa and the surface, and a deep mode with no boundary layer convergence. The model is based on the argument expressed in the authors’ companion paper that boundary layer convergence can be usefully viewed as a forcing on deep convection, not just a result thereof. The shallow mode is either specified from satellite observations or modeled using a simple mixed-layer model that has SST as well as 850-hPa geopotential height, winds, and temperature as boundary conditions. The deep-mode amplitude is empirically shown to be proportional to a simple measure of conditional instability in convecting regions, and is determined by the constraint that radiative cooling must balance adiabatic warming in subsidence regions. This two-mode model is tested against a reanalysis-derived dry static energy budget and in a reanalysis-independent framework based on satellite-derived surface convergence and using SST as a proxy for conditional instability. It can predict the observed annual mean and seasonal cycle of rainfall, vertical motion, and diabatic heating profiles across the tropical oceans with significantly more skill than optimized predictions using a thresholded linear relationship with SST. In most warm-ocean regions, significant rainfall only occurs in regions of monthly-mean boundary layer convergence. In such regions, deep-mode amplitude and rainfall increase linearly with SST, with an additional rainfall contribution from the shallow mode directly tied to boundary layer convergence. This second contribution is significant mainly in the east and central Pacific ITCZ, where it is responsible for that region’s “bottom-heavy” vertical-velocity, diabatic heating, and cloud profiles. [ABSTRACT FROM AUTHOR]

The wintertime Pacific–North American (PNA) teleconnection pattern has previously been shown to influence springtime snow conditions over portions of North America. This paper develops a more complete physical understanding of this linkage across the continent, using a recently released long-term, continental-scale gridded North American snow depth dataset and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis data. An empirical orthogonal function–based filtering process is used to identify and isolate the interannual snow depth variations associated with PNA. Then linear and partial correlations are employed to investigate the physical mechanisms that link winter PNA with spring snow depth. In the positive phase of PNA, the enhanced PNA pressure centers lead to warmer temperatures over northwestern North America and less precipitation at midlatitudes. The temperature and precipitation pathways act independently and in distinct geographical regions, and together they serve to reduce winter snow depth across much of North America. Winter anomalies in the snow depth field then tend to persist into spring. Dynamic mechanisms responsible for the PNA-influenced North American precipitation and temperature anomalies, involving moisture transport and cold air intrusions, are confirmed in this study and also extended to continental snow depth anomalies. [ABSTRACT FROM AUTHOR]

Long-lived coherent vortices located near the tropopause are often found over polar regions. Although these vortices are a commonly observed feature of the Arctic, and can have lifetimes longer than one month, little is known about the mechanisms that control their evolution. This paper examines mechanisms of intensity change for a cyclonic tropopause polar vortex (TPV) using an Ertel potential vorticity (EPV) diagnostic framework. Results from a climatology of intensifying cyclonic TPVs suggest that the essential dynamics are local to the vortex, rather than a consequence of larger-scale processes. This fact motivates a case study using a numerical model to investigate the role of diabatic mechanisms in the growth and decay of a particular cyclonic vortex. A component-wise breakdown of EPV reveals that cloud-top radiational cooling is the primary diabatic mechanism that intensifies the TPV during the growth phase. Increasing amounts of moisture become entrained into the vortex core at later times near Hudson Bay, allowing the destruction of potential vorticity near the tropopause due to latent heat release to become comparable to the radiational tendency to create potential vorticity. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach to stochastically generating rainfall sequences that can take into account natural climate phenomena, such as the El Niño–Southern Oscillation and the interdecadal Pacific oscillation. The approach is also amenable to modeling projected affects of anthropogenic climate change. The method uses a relatively new technique, empirical mode decomposition (EMD), to decompose a historical rainfall series into several independent time series that have different average periods and amplitudes. These time series are then recombined to form an intradecadal time series and an interdecadal time series. After separate stochastic generation of these two series, because they are independent, they can be recombined by summation to form a replicate equivalent to the historical data. The approach was applied to generate 6-monthly rainfall totals for six rainfall stations located near Canberra, Australia. The cross correlations were preserved by carrying out the stochastic analysis using the Matalas multisite model. The results were compared with those obtained using a traditional autoregressive lag-one [AR(1)], and it was found that the new EMD stochastic model performed satisfactorily. The new approach is able to realistically reproduce multiyear–multidecadal dry and wet epochs that are characteristic of Australia’s climate and are not satisfactorily modeled using traditional stochastic rainfall generation methods. The method has two advantages over the traditional AR(1) approach, namely, that it can simulate nonstationarity characteristics in the historical time series, and it is easy to alter the decomposed time series components to examine the impact of anthropogenic climate change. [ABSTRACT FROM AUTHOR]

This paper analyzes the changes in the precipitation climatologies of Europe for the periods 1960–90 and 2070–2100 using a heterogeneous set of regional climate models (RCMs). The authors used the Climatic Research Unit (CRU) database to define a precipitation climatology for current climate conditions (1960–90), then compare the estimates with the RCMs’ simulations for the same period using spectral analysis. After the authors evaluated the performance of the models compared with validation data for current climate, they calculated the future climate spectra (2070–2100). Changes in the future climate have been evaluated in terms of differences in the phase and amplitude of the annual cycle with respect to present conditions. The results show that models provide consistent results and that under the A2 scenario (increased greenhouse gases conditions) precipitation climatologies in Europe are expected to suffer noticeable changes, the most important being a strengthening of the annual cycle in most of the Atlantic coastal areas of the continent. While total amounts of rainfall might undergo little change, the consequences of changes in the seasonal distribution of precipitation will strongly affect both ecosystems and human activities. Differences were also found in the probability distribution function (pdf) of precipitation, indicating an overall increase in the frequency of precipitation-related hazards in Europe. [ABSTRACT FROM AUTHOR]

This paper uses the NCAR Community Atmospheric Model to show the influence of Atlantic warm pool (AWP) variability on the summer climate and Atlantic hurricane activity. The model runs show that the climate response to the AWP’s heating extends beyond the AWP region to other regions such as the eastern North Pacific. Both the sea level pressure and precipitation display a significant response of low (high) pressure and increased (decreased) rainfall to an anomalously large (small) AWP, in areas with two centers located in the western tropical North Atlantic and in the eastern North Pacific. The rainfall response suggests that an anomalously large (small) AWP suppresses (enhances) the midsummer drought, a phenomenon with a diminution in rainfall during July and August in the region around Central America. In response to the pressure changes, the easterly Caribbean low-level jet is weakened (strengthened), as is its westward moisture transport. An anomalously large (small) AWP weakens (strengthens) the southerly Great Plains low-level jet, which results in reduced (enhanced) northward moisture transport from the Gulf of Mexico to the United States east of the Rocky Mountains and thus decreases (increases) the summer rainfall over the central United States, in agreement with observations. An anomalously large (small) AWP also reduces (enhances) the tropospheric vertical wind shear in the main hurricane development region and increases (decreases) the moist static instability of the troposphere, both of which favor (disfavor) the intensification of tropical storms into major hurricanes. Since the climate response to the North Atlantic SST anomalies is primarily forced at low latitudes, this study implies that reduced (enhanced) rainfall over North America and increased (decreased) hurricane activity due to the warm (cool) phase of the Atlantic multidecadal oscillation may be partly due to the AWP-induced changes of the northward moisture transport and the vertical wind shear and moist static instability associated with more frequent large (small) summer warm pools. [ABSTRACT FROM AUTHOR]

Following the onset of the strong El Niño of 1997–98 historical rainfall teleconnection patterns and dynamical model predictions both suggested an enhanced likelihood of drought for southern Africa, but widespread dry conditions failed to materialize. Results from a diagnostic study of NCEP–NCAR reanalysis data are reported here demonstrating how the large- and regional-scale atmospheric circulations during the 1997–98 El Niño differed from previous events. Emphasis is placed on the January–March 1998 season and comparisons with the strong 1982–83 El Niño, although composites of eight events occurring between 1950 and 2000 are also considered. In a companion paper, simulation runs from three atmospheric general circulation models (AGCMs), and forecasts from three fully coupled models are employed to investigate the extent to which the anomalous atmospheric circulation patterns during the 1997–98 El Niño may have been anticipated. Observational results indicate that the 1997–98 El Niño displayed significant differences from both the 1982–83 episode and the composite event. An unusually strong Angola low, exceptionally high sea surface temperatures (SSTs) in the western Indian and eastern tropical South Atlantic Oceans, and an enhanced northerly moisture flux from the continental interior and the western tropical Indian Ocean all appear to have contributed to more seasonal rainfall in 1997–98 over much of the southern Africa subcontinent than in past El Niño events. [ABSTRACT FROM AUTHOR]

Polarization radar offers the promise of much more accurate rainfall-rate R estimates than are possible from radar reflectivity factor Z alone, not only by better characterization of the drop size distribution, but also by more reliable correction for attenuation and the identification of hail. However, practical attempts to implement retrieval algorithms have been hampered by the difficulty in coping with the inherent noise in the polarization parameters. In this paper, a variational retrieval scheme is described that overcomes these problems by employing a forward model for differential reflectivity Zdr and differential phase shift φdp and iteratively refining the coefficient a in the relationship Z = aRb such that the difference between the forward model and the measurements is minimized in a least squares sense. Two methods are used to ensure that a varies smoothly in both range and azimuth. In range, a is represented by a set of cubic-spline basis functions; in azimuth, the retrieval at one ray is used as a constraint on the next. The result of this smoothing is that the retrieval is tolerant of random errors in Zdr of up to 1 dB and in φdp of up to 5°. Correction for attenuation is achieved simply and effectively by including its effects in the forward model. If hail is present then the forward model is unable to match the observations of Zdr and φdp simultaneously. This enables a first pass of the retrieval to be used to identify the radar pixels that contain hail, followed by a second pass in which the fraction of the Z in those gates that is due to hail is retrieved, this time with the scheme being able to forward-model both Zdr and φdp accurately. The scheme is tested on S-band radar data from southern England in cases of rain, spherical hail, oblate hail, and mixtures of rain and hail. It is found to be robust and stable, even in the presence of differential phase shift on backscatter. [ABSTRACT FROM AUTHOR]

Since the ISV of the convection is an intermittent phenomenon, the local mode analysis (LMA) technique is used to detect only the ensemble of intraseasonal events that are well organized at large scale. The LMA technique is further developed in this paper in order to perform multivariate analysis given patterns of SST and surface wind perturbations associated specifically with these intraseasonal events. During boreal winter, the basin-scale eastward propagation of the convective perturbation is present only over the Indian Ocean Basin. The intraseasonal SST response to convective perturbations is large and recurrent over thin mixed layer regions located north of Australia and in the Indian Ocean between 5° and 10°S. By contrast, there is little SST response in the western Pacific basin and no clear eastward propagation of the convective perturbation. During boreal summer, the SST response is large over regions with thin mixed layers located north of the Bay of Bengal, in the Arabian Sea, and in the China Sea. The northeastward propagation of the convective perturbation over the Bay of Bengal is associated with a standing oscillation of the SST and the surface wind between the equator and the northern part of the bay. In fact, many intraseasonal events mostly concern a single basin, suggesting that the interbasin organization is not a necessary condition for the existence of coupled intraseasonal perturbations of the convection. The perturbation of the surface wind tends to be larger to the west of the large-scale convective perturbation (like for a Gill-type dynamical response). For eastward propagating perturbations, the cooling due to the reinforcement of the wind (i.e., surface turbulent heat flux) thus generally lags the radiative cooling due to the reduction of the surface solar flux by the convective cloudiness. This large-scale Gill-type response of the surface wind also cools the surface to the west of the basin (northwest Arabian Sea and northwest Pacific Ocean), even if the convection is locally weak. An intriguing result is a frequently occurring small delay between the maximum surface wind and the minimum SST. Different explanations are invoked, like a rapid surface cooling due to the vanishing of an ocean warm layer (diurnal surface warming due to solar radiation in low wind conditions) as soon as the wind increases. [ABSTRACT FROM AUTHOR]

In terms of the effects of future climate change upon society, some of the most important parameters to estimate are associated with changing risks of extreme rainfall events, both floods and droughts. However, such aspects of the climate system are hard to estimate well using general circulation models (GCMs)—in particular, for a small mountainous landmass such as New Zealand. This paper describes a downscaling technique using broad-scale changes simulated by GCMs to select past analogs of future climate. The analog samples are assumed to represent an unbiased sample of future rainfall and are used to develop detailed descriptions of rainfall statistics using hidden semi-Markov models of rainfall breakpoint information. Such models are used to simulate long synthetic rainfall time series for comparison with the historical record. Results for three New Zealand sites show overall increases in rainfall with climate change, brought about largely by an increased frequency of rainfall events rather than an increase in rainfall intensity. There was little evidence for significant increases in high-intensity short-duration rainfalls at any site. Such results suggest that, although regional increases of rainfall are consistent with expected future climate changes, it may be that circulation changes, rather than temperature (and vapor pressure) changes, will be the more important determinant of future rainfall distributions, at least for the coming few decades. [ABSTRACT FROM AUTHOR]

Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive ground validation (GV) program. Since the launch of TRMM in late 1997, standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground-based radar data adjusted to the gauge accumulations from four primary sites. As part of the NASA TRMM GV program, effort is being made to evaluate these GV products. This paper describes the product evaluation effort for the Melbourne, Florida, site. This effort allows us to evaluate the radar rainfall estimates, to improve the algorithms in order to develop better GV products for comparison with the satellite products, and to recognize the major limiting factors in evaluating the estimates that reflect current limitations in radar rainfall estimation. Lessons learned and suggested improvements from this 8-yr mission are summarized in the context of improving planning for future precipitation missions, for example, the Global Precipitation Measurement (GPM). [ABSTRACT FROM AUTHOR]

Three interrelated climate phenomena are at the center of the Climate Variability and Predictability (CLIVAR) Atlantic research: tropical Atlantic variability (TAV), the North Atlantic Oscillation (NAO), and the Atlantic meridional overturning circulation (MOC). These phenomena produce a myriad of impacts on society and the environment on seasonal, interannual, and longer time scales through variability manifest as coherent fluctuations in ocean and land temperature, rainfall, and extreme events. Improved understanding of this variability is essential for assessing the likely range of future climate fluctuations and the extent to which they may be predictable, as well as understanding the potential impact of human-induced climate change. CLIVAR is addressing these issues through prioritized and integrated plans for short-term and sustained observations, basin-scale reanalysis, and modeling and theoretical investigations of the coupled Atlantic climate system and its links to remote regions. In this paper, a brief review of the state of understanding of Atlantic climate variability and achievements to date is provided. Considerable discussion is given to future challenges related to building and sustaining observing systems, developing synthesis strategies to support understanding and attribution of observed change, understanding sources of predictability, and developing prediction systems in order to meet the scientific objectives of the CLIVAR Atlantic program. [ABSTRACT FROM AUTHOR]

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 global coupled climatic model has been used to generate a 10 000-yr simulation of “present” climate. The resultant dataset has been used to investigate a number of aspects of extremes associated with annual mean rainfall. Multimillennial time series of normalized rainfall amounts for selected points are used to highlight secular variability, spatial variations, and the differences between pluvial and drought conditions. Global distributions are also presented for selected rainfall characteristics, including the frequency of occurrence of specified rainfall anomalies with annual durations, the frequency of occurrence of 5-yr sequences of specified rainfall anomalies, and the maximum and minimum normalized rainfall amounts attained in the simulation. Such features cannot be obtained from observations because of their limited duration. A case study is also made of a megadrought over the southwestern United States, together with an analysis of the associated causal mechanisms. Given the exclusion of all external forcing from the model, it is concluded that the extreme annual mean rainfall extremes presented in the paper are attributable to stochastic events. [ABSTRACT FROM AUTHOR]

Intercomparisons of satellite rainfall products have historically focused on the issue of global mean biases. Regional and temporal variations in these biases, however, are equally important for many climate applications. This has led to a critical examination of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Because of the time-dependent nature of these biases, it is not possible to apply corrections based on regionally defined characteristics. Instead, this paper seeks to relate PR–TMI differences to physical variables that can lead to a better understanding of the mechanisms responsible for the observed differences. To simplify the analysis, issues related to differences in rainfall detection and intensity are investigated separately. For clouds identified as raining by both sensors, differences in rainfall intensity are found to be highly correlated with column water vapor. Adjusting either TMI or PR rain rates based on this simple relationship, which is relatively invariant over both seasonal and interannual time scales, results in a 65%–75% reduction in the rms difference between seasonally averaged climate rainfall estimates. Differences in rainfall detection are most prominent along the midlatitude storm tracks, where widespread, isolated convection trailing frontal systems is often detected only by the higher-resolution PR. Conversely, over the East China Sea clouds below the ∼18-dBZ PR rainfall detection threshold are frequently identified as raining by the TMI. Calculations based on in situ aerosol data collected south of Japan support a hypothesis that high concentrations of sulfate aerosols may contribute to abnormally high liquid water contents within nonprecipitating clouds in this region. [ABSTRACT FROM AUTHOR]

Plotting order-ranked data is a standard technique that is used in estimating the probability of extreme weather events. Typically, observations, say, annual extremes of a period of N years, are ranked in order of magnitude and plotted on probability paper. Some statistical model is then fitted to the order-ranked data by which the return periods of specific extreme events are estimated. A key question in this method is as follows: What is the cumulative probability P that should be associated with the sample of rank m? This issue of the so-called plotting positions has been debated for almost a century, and a number of plotting rules and computational methods have been proposed. Here, it is shown that in estimating the return periods there is only one correct plotting position: P = m/(N + 1). This formula predicts much shorter return periods of extreme events than the other commonly used methods. Thus, many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated. [ABSTRACT FROM AUTHOR]

Moisture is transported in South America westward from the tropical Atlantic Ocean to the Amazon basin, and then southward toward the extratropics. A regional intensification of this circulation to the east of the Andes Mountains is called the South American low-level jet (SALLJ), with the strongest winds found over eastern Bolivia. SALLJ is present all year and channels moisture to the La Plata basin, which is analogous to the better-known Amazon basin in terms of its biological and habitat diversity, and far exceeds the latter in its economic importance to southern and central South America in terms of hydroelectricity and food production. The relatively small SALLJ spatial scale (compared with the density of the available sounding network) has a limited understanding of and modeling capability for any variations in the SALLJ intensity and structure as well as its possible relationship to downstream rainfall. The SALLJ Experiment (SALLJEX), aimed at describing many aspects of SALLJ, was carried out between 15 November 2002 and 15 February 2003 in Bolivia, Paraguay, central and northern Argentina, western Brazil, and Peru. Scientists, collaborators, students, National Meteorological Service personnel, and local volunteers from South American countries and the United States participated in SALLJEX activities in an unprecedented way, because SALLJEX was the most extensive meteorological field activity to date in subtropical South America, and was the first World Climate Research Program/ Climate Variability and Prediction Program international campaign in South America. This paper describes the motivation for the field activity in the region, the special SALLJEX observations, and SALLJEX modeling and outreach activities. We also describe some preliminary scientific conclusions and discuss some of the remaining questions. [ABSTRACT FROM AUTHOR]