Windows 2000 Build 1946 Iso
Nelson Suggs
Windows 2000 build 1946 is the official Beta 3 Release Candidate 0 build of Windows 2000, released by Microsoft on 17 December 1998[1] and leaked the same day by Pirates with Attitudes. It is notably the earliest available build of Windows 2000 to introduce the final name across varying operating system surfaces, including branding.
The Server edition of this build was scene-leaked by warez group Pirates with Attitudes two days prior on 17 December 1998 in the form of a 3310-megabyte archive set containing the full disc image, although the currently available copy is missing several archive parts and is therefore incomplete.
Windows 2000 build 1946.1 is the Official Beta 3 Release Candidate 0 build of Windows 2000. The Server edition of this build was shared online as well, but is missing several archive parts and is thus incomplete. A reference to an Alpha compile of this build can be found in file drvman_reg.bat included with System Stress 1.0 for Windows NT 4.0 and Windows 2000
I tested build 2031.1 Professional and it's 1946 with a "setup.exe" version that's labelled as 2031. There's even an "info.txt" readme that states it is 1946, and only the folders from the root directory of the ISO has changed, the rest still uses the build 1946 version. Can someone remove or rename it?
Another option is to google the sums.
Get the tools "md5sum" and "sha256sum", there are versions for windows, linux and dos.
Do this:
"md5sum file.iso"
"sha256sum file.iso"
and feed the output to google.
Maybe someone else did this job already and maybe this leads to answers.
There is a big problem with these ISO's. I keep getting the "Guru Mediation" error when I run the VM, and the Guru Mediation error will appear when the text "Setup is starting Windows 2000" is on the setup screen. I am using Virtualbox 5.2.
Guru meditations aren't a windows thing. Would that be on the host? If so, whatever it is may not take well to VBox 5.2. I've had significant stability issues with VirtualBox on Windows 7 recently as well.
Yes, those crashes do happen on the host, but it just happens when a critical error has occurred during the virtual machine execution. It may happen if there's issues with memory and stuff like that. Doesn't affect the host, though.
Surface air temperatures in most European regions have increased during the twentieth century (Houghton et al. 2001). In line with the characteristics of global temperature rise (Jones et al. 1999b; Karl et al. 2000), the European rate of change has been highest in the last quarter of the century (Klein Tank et al. 2002). The warming is projected to continue and is likely to be accompanied by changes in extreme weather and climate events (Houghton et al. 2001). Yet, little is known quantitatively about the nature of these changes. In this context, it is relevant to learn how the past warming affected the occurrence of temperature extremes, or whether the past warming was accompanied by detectable changes in precipitation extremes. Studies on these issues are receiving increased attention in the last few years (Easterling et al. 2000; Meehl et al. 2000).
Although changes in extreme temperature and precipitation events have been analyzed for individual European countries and stations (see, e.g., Forland et al. 1998; Tuomenvirta et al. 2000; Moberg et al. 2000; Brunetti et al. 2000; Osborn et al. 2000; Yan et al. 2002), a coherent picture for Europe as a whole is lacking. The main reason is the limited spatial coverage of the high time-resolution European datasets used in such studies. The second reason is that until recently no accepted standardization existed in the definitions of climate extremes, which has made it difficult to compare the results of different studies. This situation has changed now. The objective of the present study is to investigate the trends in some of the recently defined (Peterson et al. 2001) indices of temperature and precipitation extremes using the European Climate Assessment (ECA) daily dataset (Klein Tank et al. 2002).
Section 2 describes the criteria for station selection from the daily ECA dataset and section 3 the selection of indices from the Peterson et al. (2001) list. The procedures for estimating trend values for Europe-average indices, comparing cold to warm extremes, and comparing precipitation extremes to total amount are outlined in section 4. Section 5 presents the observed trends for temperature and precipitation. In section 6 the results are discussed. Section 7 summarizes the conclusions.
A total of 86 daily temperature series and 151 daily precipitation series from the ECA dataset survived all selection criteria. Only these station series (Fig. 1) were used in the present study to calculate the indices.
Apart from trends for each individual ECA station, trends were also calculated for Europe as a whole. The European trends were obtained from Europe-average indices series calculated as the arithmetic average of the annual indices values at all 86 temperature stations or 151 precipitation stations. Annual values in the Europe-average indices series based on less than 75% of the stations were omitted when calculating the European trends.
Because of the nonuniform spatial distribution of ECA stations over Europe, areas with a higher density of stations are overrepresented in the Europe average. Proper area weighing methods would include gridding of time series. Such methods are not applied in this study. Based on the comparison for mean temperature in Klein Tank et al. (2002), we estimate that the trends in our Europe-average indices series would agree within 10% with trends in area-weighed indices series.
Annual day-count indices based on percentile thresholds are expressions of anomalies relative to the local climate. Consequently, the value of the thresholds is site specific. Such indices allow for spatial comparisons, because they sample the same part of the temperature and precipitation (probability density) distributions at each station. Annual day-count indices based on absolute thresholds are less suitable for spatial comparisons of extremes than those based on percentile thresholds. The reason is that, over an area as large as the European continent, annual day-count indices based on absolute thresholds may sample very different parts of the temperature and precipitation distributions. This implies that in another climate regime, the variability in such indices readily stems from another season. For instance, year-to-year variability in frost-day counts (days with minimum temperature < 0C) relates to the variability in the spring and autumn temperatures for the northern part of Europe, whereas in the southern part of Europe annual variability in frost-day counts is determined by winter temperature variability (Heino et al. 1999). Likewise, the threshold of 25C in the definition of summer days (days with maximum temperature > 25C) samples variations in summer temperatures in the north and variations in spring and autumn temperatures in the south.
Percentile thresholds were also used by Jones et al. (1999a) and Horton et al. (2001) for determining the frequencies of temperature extremes. Contrary to our approach, their method accounts parametrically rather than empirically for the annual cycle of thresholds. As argued by Yan et al. (2002), the effect on the indices in Table 1 of using either empirical methods for percentile calculations or parametric methods relying on distributions is small.
The index R95%tot, that is, the fraction of annual precipitation amount due to very wet days, has been introduced in our study to explore the supposed amplified response of the extreme precipitation events relative to the change in total amount (Groisman et al. 1999; see also Houghton et al. 2001). Indices like R95%tot are suitable to analyze such changes in the tail of the precipitation distribution, as they implicitly take into account the trends in the total amount. Similar to the indices used by Osborn et al. (2000), but unlike many other indices of precipitation extremes (Haylock and Nicholls 2000), the R95%tot index is not sensitive to changes in the number of wet days.
In a second step of the analysis of precipitation extremes, we investigated to what extent the increase or decrease in the annual amount can be attributed to an increase or decrease in the number of very wet days R95% or the amount that falls at these days. The sign of the station trends for the annual amount were compared with the sign of the trends in the R95%tot index for the fraction of the annual amount due to very wet days. At stations where the annual amount increases, positive R95%tot trends are indicative of a disproportionate large contribution of the extremes to this wetting. On the other hand, at stations where the annual amount decreases, positive R95%tot trends indicate that the very wet days are less affected than the other wet days. Negative R95%tot trends indicate a smaller than proportional contribution of very wet days to wetting or drying.
This result implies that for the two subperiods asymmetric temperature change can be detected, whereas for the entire period asymmetry is undetectable. Asymmetry leads to a narrowing of the temperature distributions for the cooling subperiod and to a widening of the temperature distributions for the warming subperiod. Comparison with the trend in the median indicates that the asymmetry can mainly be attributed to the fact that the warming trends in TN10% and TX10% lag behind.
Table 5 shows that from the 35 (73) stations for which the increase in the annual amount is significant at the 5% (25%) level, 11 (46) stations also have a significant increase in R95%tot. No station with a decrease in the annual amount significant at the 5% level shows a change in R95%tot significant at the 5% level. This means that a signal of a disproportionate large change in the extremes relative to the total amount is present, but this signal is only apparent in wetting areas. In drying areas, no signal is found.
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