Severe storm electricity

Successful ground truth support of U-2 overflights was been accomplished. Data have been reduced for 4 June 1984 and some of the results have been integrated into some of MSFC's efforts. Staccato lightning (multiply branched, single stroke flash with no continuing current) is prevalent within the rainfree region around the main storm updraft and this is believed to be important, i.e., staccato flashes might be an important indicator of severe storm electrification. Results from data analysis from two stations appear to indicate that charge center heights can be estimated from a combination of intercept data with data from the fixed laboratory at NSSL. An excellent data base has been provided for determining the sight errors and efficiency of NSSL's LLP system. Cloud structures, observable in a low radar reflectivity region and on a scale smaller than is currently resolved by radar, which appear to be related to electrical activity are studied

Significance Arithmetic for Fortran

Significance tracing arithmetic for Fortra

The diurnal nature of future extreme precipitation intensification

Short‐duration, high‐impact precipitation events in the extratropics are invariably convective in nature, typically occur during the summer, and are projected to intensify under climate change. The occurrence of convective precipitation is strongly regulated by the diurnal convective cycle, peaking in the late afternoon. Here we perform very high resolution (convection‐permitting) regional climate model simulations to study the scaling of extreme precipitation under climate change across the diurnal cycle. We show that the future intensification of extreme precipitation has a strong diurnal signal and that intraday scaling far in excess of overall scaling, and indeed thermodynamic expectations, is possible. We additionally show that, under a strong climate change scenario, the probability maximum for the occurrence of heavy to extreme precipitation may shift from late afternoon to the overnight/morning period. We further identify the thermodynamic and dynamic mechanisms which modify future extreme environments, explaining both the future scaling's diurnal signal and departure from thermodynamic expectations

Irrigation system performance assessment and diagnosis

Performance evaluation / Irrigation programs / Irrigation management / Irrigation systems / Case studies / Hydraulics / Management / Environmental effects / Asia / Africa / South America

Solar vector magnetograph for Max 1991 programs

An instrument for measuring solar magnetic fields is under construction. Key requirements for any solar vector magnetograph are high spatial resolution, high optical throughput, fine spectral selectivity, and ultralow instrumental polarization. An available 25 cm Cassegrain telescope will provide 0.5 arcsec spatial resolution. Spectral selection will be accomplished with a 150 mA filter based on electrically tunable solid Fabry-Perot etalon. Filter and polarization analyzer design concepts for the magnetograph are described in detail. The instrument will be tested at JHU/APL, and then moved to the National Solar Observatory in late 1988. It will be available to support the Max 1991 program

Tempting long-memory - on the interpretation of DFA results

We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than α=0.5 for large scales does not necessarily imply long-memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocorrelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously

Trend assessment: applications for hydrology and climate research

The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend

Statistical characteristics of surrogate data based on geophysical measurements

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales

a multiple linear regression model

The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month. Furthermore index-specific signals are calculated to estimate the contribution to temperature anomalies caused by each individual teleconnection pattern. To this extent, an observational product of monthly mean temperature (E-OBS), as well as monthly time series of teleconnection indices (CPC, NOAA) for the period 1951–2010 are evaluated. The stepwise regression approach is used to build grid cell based models for each month on the basis of the five most important teleconnection indices (NAO, EA, EAWR, SCAND, POLEUR), which are motivated by an exploratory correlation analysis. The temperature links are dominated by NAO and EA in Northern, Western, Central and South Western Europe, by EAWR during summer/autumn in Russia/Fenno-Scandia and by SCAND in Russia/Northern Europe; POLEUR shows minor effects only. In comparison to the climatological forecast, the presented linear regression models improve the temperature modelling by 30–40 % with better results in winter and spring. They can be used to model the spatial distribution and structure of observed temperature anomalies, where two to three patterns are the main contributors. As an example the estimated temperature signals induced by the teleconnection indices is shown for February 2010