Sentences with phrase «calculate the trends over»

Download the GISS temperature anomalies from http://data.giss.nasa.gov/gistemp/ and calculate the trend over the last 30 years.

One can also calculate the trends over successive periods of, say, ten years, with start - points separated by one year.

Probably the easiest way (no linear regression required) to compare IPCC projections to what happened over the period 1990 -2005 is to take averages (eg over 5, 7, 9, or 11 years) centered on 1990 and 2005 and use the difference to calculate the trend over that period.

Not exact matches

For each forex pair, a sequence is calculated based on its up - trends or downtrends over an expiry period thus determining its resistance and support levels.

As I have mentioned previously I simply run a nightly scan of Long and Short stock candidates hitting 52 week highs / lows and keep note of these stocks and over the course of the coming days and weeks I look for which stocks keep hitting the parameters of my scans before taking a closer look at the chart, once I see there is a clean smooth trend be it going up or down I then calculate from that afternoons closing price and where the stop loss would need to be positioned on the first day the trade is placed in line with my risk management and then simply wait for the open the following day to open the trade then my system does the rest.

The idea is to define area masks as a function of the emissions data and calculate the average trend — two methods were presented (averaging over the area then calculating the trend, or calculating the trends and averaging them over the area).

Once again, a few short months later, a followup article was published by one of us (Mann, 2004) that invalidated the Soon et al (2004) conclusions, demonstrating (with links to supporting Matlab source codes and data) how (a) the authors had, in an undisclosed manner, inappropriately compared trends calculated over differing time intervals and (b) had not used standard, objective statistical criteria to determine how data series should be treated near the beginning and end of the data.

I looked at eight CMIP 5 models whose output I had ready access to and calculated linear trends of potential intensity over the period 2006 - 2100 under the RCP 8.5 emissions pathway.

-- I calculated potential intensity trends over the period 1980 - 2012 & The disparity between the reanalysis potential intensity trends over the past 30 years and the projected trends over this century suggests either that most of the observed increase in potential intensity (and actual intensity of high category storms) is due to natural variability,....»

One merely calculates the least - squares linear - regression trend over successively longer periods to see whether the slope of the trend progressively increases (as it must if the curve is genuinely exponential) or whether, instead, it progressively declines towards linearity (as it actually does).

I calculated potential intensity trends over the period 1980 - 2012 using three different reanalysis products: NASA's MERRA, the European Center's ERA Interim, and the NCAR / NCEP reanalysis.

Calculating the running mean temperature — over periods of 12, 60 and 132 months — provides a way to see long - term trends behind variability.

The trend (heavy black line) calculated over the period 1895 - 2012 is equal to an increase of 1.5 °F.

The clue is in the third column of the table, which shows the trend calculated only over the map cells where the datasets all share common coverage.

Then I calculated the trend in the adjustment averaged over the stations in each grid cell on the globe, to determine whether the adjustments were increasing or decreasing the temperature trend.

RC11 takes a short term trend, along with an estimate of variability, calculates the probability that particular thresholds will be exceeded over a 10 year time frame.

The figures for the date centre are calculated slightly differently, but they too show no trend over the period in question.

Perhaps you could calculate the maximum estimated trend over the last 16 years.

Climate models are what happens when you calculate changes over a long enough period of time for the fluctuations in weather to average out so that you can see the underlying trend.

If we calculate the linear trend for the Good subset over the entire 1895 - 2011 period, we would get a net «warming» trend.

However, over such a short timescale, the forcings are not significantly non-linear, and thus calculating the linear trend is appropriate.

Since we already have data for the full year of 2011, I have calculated the warming trend required for the next 9 years to reach 0.2 deg C over the entire 20 - year period (and that is a linear warming rate of around 0.556 degC per decade, or a linear warming of 0.5 degC over the 9 - year period that is still left.

Because you are fitting to look for a trend * after * selecting the data that looks flat, the real 95 % confidence interval of the trend in temperature (or ocean heat content) over any of these intervals is much larger than what you are presumably calculating.

For the larger 50S — 90S region a trend over 1880 — 2015 can be calculated, at 0.03 °C / decade, if a minimum of 15 % of valid data points is accepted.

If the required minimum is reduced to 20 %, trends can be calculated over 1992 - 2015, for which they are 0.029 °C / decade for SST, and 1.5 % higher at 0.030 °C / decade for tas.

The SSTs from the simulations with these 18 models were used to calculate the Nino3.4 trends over sliding 15 - year periods for each of the simulations, from which the «best» and «worst» composites were derived for each of the sliding 15 - year time periods.

He calculated for each station the trend for each month of the year over the station lifetime.

Whatever one feels about each of these years having only a single month's worth of data, I think it is reasonable to say we shouldn't calculate linear trends over 37 points of data when 2 of those points are separated from the rest by 8 years.

They found that the warming in the data - sparse regions was progressing faster than the global average (especially during the past couple of years) and that when they included the data that they derived for these regions in the computation of the global average temperature, they found the global trend was higher than previously reported — just how much higher depended on the period over which the trend was calculated.

Depending on which particular set of data you looked at, and how you calculated trends, there was an argument that temperature rises had slowed over a period of about 15 years.

C / decade and the simulated ensemble mean over the models, calculated from the grid boxes of the models where observations exist (which is flawed in my opinion, since excluding of mostly the high latitudes from the model data may emphasize a warm bias in lower latitudes in the models making them appear warmer than they are, but a possible cold bias of the global observations data set is not excluded in this way) had a trend of 0.3 deg.

I calculated the least squares trend over the first 20 years in each set to forecast the subsequent 20 year trend and calculated the relative forecast skill of this approach against the assumption of no trend.

In fact, we performed a simple but powerful statistical test before drawing that conclusion: we calculated the linear - regression trends over successively longer periods to see whether the slope of the trend progressively increased (as it must if the curve is genuinely exponential); but, in recent years, the trend has ceased to increase.

Monckton — quoted by Bickmore «One merely calculates the least - squares linear - regression trend over successively longer periods to see whether the slope of the trend progressively increases (as it must if the curve is genuinely exponential) or whether, instead, it progressively declines towards linearity (as it actually does).»

I have also calculated the trend statistics from the yearly results graphed in the links, and while the p.values can be impressive over the range of forcing, as noted above the trends calculated within parts of the range can change dramatically.

Supplementary Information (SI), along with the values I calculate from their 1996 — 2005 GMST data, averaged ERF data for 2000 and ocean heat uptake data (taking the trend over 1996 — 2005), and alternatively by accurately digitising ERF ΔF and ΔF − ΔQ values in Marvel et al..

Calculating a «running average» over these longer timescales allows one to more easily see long - term trends.

His unspoken argument is that you have to take a temperature trend over complete cycle (s) in order to remove the cyclical effect: «To remove the warming rate due to the multidecadal oscillation of about 60 years cycle, least squares trend of 60 years period from 1945 to 2004 is calculated ``.

The globally averaged combined land and ocean surface temperature data as calculated by a linear trend show a warming of 0.85 [0.65 to 1.06] °C over the period 1880 to 2012

We calculate rental price trends as the average annual change in market rent paid in the neighborhood over the latest 5 years.

Long term median sale prices, that is those calculated over the 12 month period are deemed to be indicative of the trend in property prices, whilst quarterly median sale prices are more reflective of what types of properties sold.