40

The value -999 is likely the "fill value" used in the dataset when data is missing or is not being properly filtered or handled when displayed. In the specific case on the website you cite, it is likely a problem with the algorithm for wind chill (the "feels like" temperature this time of year). It isn't a physical value and only means the value is ...


31

It's a Campbell-Stokes sunshine recorder, used to record the times at which the sun is shining. It acts as a lens, focusing the sunlight onto a piece of card. If the sun is shining, the focused beam will burn a hole through the card at that point. Here's a closer view of a sunshine recorder in Wendelstein, Bavaria: Source: Wikimedia Commons And here's a ...


28

All numerical atmospheric models are built around calculations derived from primitive equations that describe atmospheric flow. Vilhelm Bjerknes discovered the relationships and thereby became the father of numerical weather prediction. Conceptually, the equations can be thought of as describing how a parcel of air would move in relationship to its ...


26

In the US, meteorologists forecast the probability of ANY amount of precipitation falling. The minimum amount of that we deem acceptable to meet this criteria is .01". So, we are forecasting the probability of one hundredth of an inch of precipitation to fall at a location. We look at observational data from ground stations, satellites, and computer ...


20

Weather models (or, as they are more commonly called in the field, atmospheric models) are computer programs that read in input data (initial conditions) and solve partial differential equations to produce a future state of the atmosphere. Although @JonEricson provides an overall good but anecdotal summary of what models do, here I describe the exact steps ...


18

The major differences between weather and climate models are many. At their core lie the same set of primitive equations, but from here there are many differences. A weather model only (skillfully) predicts about 10 days into the future, while a climate model integrates forward in time for hundreds of years. The main difference here is that in a weather ...


15

This answer is not complete, but it is a start. One of the most significant differences is: Weather models use measurements, whereas climate models do not Put another way: a weather model is an initial value problem. The initial values that go in are of essential importance for the result to be correct. A climate model solves what is primarily a ...


15

The hydrostatic approximation begins with the full 3-D momentum equation (Navier-Stokes) and through scale analysis the vertical momentum equation reduces to: $$\dfrac{\partial p}{\partial z} = -\rho g$$ This is a balance between the vertical pressure gradient force and gravity with no net acceleration. This tends to hold for atmospheric phenomena that ...


14

The intensity of a rainstorm does not actually cause the duration of the precipitation to be shorter. There is a strong correlation, but not in the sense you may be implying here (let me get back to that in moment). First you have to dispel the model that a cloud is a big container of water, and if it "rains harder", the cloud will run out of water quicker ...


13

For same day forecasting of afternoon thunderstorms, I'd start with the morning observations and the 12Z† model runs (I choose 12Z because that is the morning here in the Americas. For Europe you might be more interested in the 00Z or 06Z runs to start your day). In particular I'd start with the output of the 12Z GFS and NAM, and then give that some time ...


12

This is not a complete answer. One aspect of weather models consists of Data assimilation or 4D-var. I agree that they are amazing, and the question how do they work is too broad to be answered. So I recommend you read up on data assimilation and in particular 4D-Var. Concepts are somewhat similar in inverse theory, but of much higher dimensionality. In ...


12

The equal-area projection used to create the continental U.S. grid for weather forecasting does not represent lines of latitude and longitude as straight lines. Instead, they are curved (note the Canada-USA border curvature below, which is actually along the 49th latitude parallel). An equal-area projection is often used in regional weather modeling ...


11

This is my favorite example of the difference between a hydrostatic and a non-hydrostatic code. The simulation depicts a lock exchange which you can picture as opening your window if you live in a cold place and it is winter. Inside your house, presumably, the air is warmer than the outside. So when you open the window, the cooler (heavier) air will ...


11

The major factors in temperature change from precipitation... First, fundamentally rain is falling to the ground from higher in the sky. Precipitation typically comes from a location where it is colder compared to us on the ground (the troposphere generally is cooler with height, on average around 6.5 Celsius\km.). Basically, the rain itself usually at ...


10

A page in the book Come Rain or Shine, A Weather Miscellany states that the cones were discontinued on June 1, 1984, due to their being superseded by radio broadcasts and other methods.


10

The probability of precipitation is most likely to mean the proportion of models in an ensemble or weather models in which precipitation was observed at a particular location over in particular time period. If you want to work out the probability of it raining during the day, the best approach would be to work out one minus the probability that there was ...


10

Email response from Alex Jacques (Programming Support - MesoWest): The exact interpretation can vary between data providers to MesoWest, but in general it refers to the amount of snow that has fallen on an "interval board". In several cases, stations have boards where snowfall is recorded, and then the board is wiped clean at set intervals (...


10

Rings wouldn't decrease the gravity much, but the exact amount would depend on the exact geometry of the rings. One reason is that the gravity of one side of the ring would partially cancel the attraction of the other side. In a similar fashion described by the Shell theorem demonstrated long ago by Newton and proving that if you are inside a shell of mass ...


9

climate data operators (CDO) define grid We define a lat-lon target grid with 1°x1° grid cell size 30x30 grid cells starting at 40°N and -10°E (=10°W): gridtype = lonlat xsize = 30 ysize = 30 xfirst = -10 xinc = 1 yfirst = 40 yinc = 1 This text is written into a text file. See section 1.3.2 CDO Manual for details and ...


8

Weather models and forecasts are governed by systems of differential equations. One starts with the current levels or values of causal variables: temperature, humidity, atmospheric pressure etc. One also has to factor in the "derivatives," or rates of change of these variables. Hence the need for differential equations, which incorporate both variables and ...


8

Disclaimer: I am not a meteorologist. To answer this question, you need to understand how a forecast is obtained. Basically, meteorologists run computer simulations that predict how weather systems might evolve from current measurements. In fact, data assimilation techniques (http://en.wikipedia.org/wiki/Data_assimilation) are commonly used, so that the ...


8

Yes, NOAA's Storm Prediction Center's soundings page is a great source for the information you are looking for, especially the skew-T diagrams and wind hodographs. This site provides current soundings as well as a past archive of 1 week. For the explanation of the soundings diagrams and numbers provided, see the soundings help page.


7

The weight of a balloon determines most of its specifications, though I'm not sure if they are generalized or specific to a manufacturer. Looking at one supplier we can get a feel for what you need. For a 1 kg payload it looks like you'd want a minimum of a 1200g balloon. According to the site linked, this 1200g balloon will have: 6.0 ft diameter at ...


7

You need a model. You could try to use Bayes theorem to build a model based upon conditional probabilities. See this reference from NOAA about weather forecasting: Probability Forecasting - Primer . If I were to try to build a Bayesian model for weather forecasting, with the intention of using a limited data set of observations I had collected myself, I ...


7

Robert Cartanio's answer makes very good points, and I'll accentuate them with some examples. Thunderstorms tend to produce "hard rain" and larger scale organized convection will have areas of hard rain and areas of weaker rain. Air-mass thunderstorms are the type of daily convection you see in Florida and elsewhere. These tend to appear somewhat ...


7

No, you've misunderstood it. There's lots of information you're missing. Every data point is information, and you've listed a tiny proportion of them. Furthermore, 4-7 km/h and 11-16 km/h aren't frequencies at all, they're wind speeds. The distance from the centre gives you the frequency. So the spike towards N means that North winds are much much more ...


7

Warning: "long-term wind-speed forecasting for generation" has (at least) two very different meanings. One refers to forecasting a distribution of wind speeds; the other refers to hour-by-hour (or half-hour by half-hour) forecasting of wind speeds. Generally, when we talk about long-term forecasting of wind speeds for wind generation, we're talking about ...


7

Typically, days with conditions which might produce local instabilities would produce a less certain forecast than a day with a stabilize pattern even if the more stable day was further in the future. For instance, tomorrow may have the potential for localized disturbances some maybe there will be thunderstorms, maybe they will not form. But two days later,...


7

Such a dataset can be found via the NCDC. Here is the main gateway. My 'go to' place for high temporal resolution data from NCDC is the 5-minute ASOS data.


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