2m dew point, ERA5

In Kelvin

Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview


Low dew point == low humidity according to wikipedia, because less vapor is need for condensation
https://en.wikipedia.org/wiki/Dew_point

Dew point		Relative humidity at 32 °C (90 °F)
Over 26 °C	Over 80 °F	73% and higher
24–26 °C	75–80 °F	62–72%
21–24 °C	70–74 °F	52–61%
18–21 °C	65–69 °F	44–51%
16–18 °C	60–64 °F	37–43%
13–16 °C	55–59 °F	31–36%
10–12 °C	50–54 °F	26–30%
Under 10 °C	Under 50 °F	25% and lower

This seems to explain that the condensation on leaves is often seen in the early morning, but not during the daytime. So, if a mutant strain has a lower dew point, it tend be in drier locations. If a strain has a higher dew point, it tends to be more humid locations

CMIP5 daily data on single levels

https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip5-daily-single-levels?tab=overview

This catalogue entry provides daily climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry.

CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:

• addressing outstanding scientific questions that arose as part of the IPCC reporting process;
• improving the understanding of the climate system;
• providing estimates of future climate change and related uncertainties;
• providing input data for the adaptation to the climate change;
• examining climate predictability and exploring the ability of models to predict climate on decadal time scales;
• evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP5 simulations:

• Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.;
• Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005.
• Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically, 2006-2100 some extended RCP experimental data is available from 2100-2300.

In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets).

The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.

More details about the products are given in the Documentation section.

DATA DESCRIPTION
Data type	Gridded
Horizontal coverage	Global
Horizontal resolution	From 0.125°x0.125° to 5°x5° depending on the model
Vertical coverage	Variables are provided in one single level, which may differ among variables
Temporal coverage	From 1850 to 2300 (shorter for some experiments)
Temporal resolution	Day
File format	NetCDF

MAIN VARIABLES
Name	Units	Description
10m wind speed	m s-1	Magnitude of the two-dimensional horizontal air velocity near the surface.
2m temperature	K	Temperature of the air near the surface.
Daily near surface relative humidity	%	Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location.
Maximum 2m temperature in the last 24 hours	K	Daily maximum near-surface air temperature.
Mean precipitation flux	kg m-2 s-1	Amount of water per unit area and time.
Mean sea level pressure	Pa	Time average of the air pressure at sea level.
Minimum 2m temperature in the last 24 hours	K	Daily minimum near-surface air temperature.
Near surface specific humidity	Dimensionless	Amount of moisture in the air near the surface divided by amount of air plus moist at that location.
Snowfall	kg m-2 s-1	Mass of water in the form of snow precipitating per unit area.
Surface solar radiation downwards	W m-2	Radiative shortwave flux of energy downward at the surface.

Record updated 2020-05-14 15:52:20 UTC

USA states geo code coordinates

https://developers.google.com/public-data/docs/canonical/states_csv

state	latitude	longitude	name
AK	63.588753	-154.493062	Alaska
AL	32.318231	-86.902298	Alabama
AR	35.20105	-91.831833	Arkansas
AZ	34.048928	-111.093731	Arizona
CA	36.778261	-119.417932	California
CO	39.550051	-105.782067	Colorado
CT	41.603221	-73.087749	Connecticut
DC	38.905985	-77.033418	District of Columbia
DE	38.910832	-75.52767	Delaware
FL	27.664827	-81.515754	Florida
GA	32.157435	-82.907123	Georgia
HI	19.898682	-155.665857	Hawaii
IA	41.878003	-93.097702	Iowa
ID	44.068202	-114.742041	Idaho
IL	40.633125	-89.398528	Illinois
IN	40.551217	-85.602364	Indiana
KS	39.011902	-98.484246	Kansas
KY	37.839333	-84.270018	Kentucky
LA	31.244823	-92.145024	Louisiana
MA	42.407211	-71.382437	Massachusetts
MD	39.045755	-76.641271	Maryland
ME	45.253783	-69.445469	Maine
MI	44.314844	-85.602364	Michigan
MN	46.729553	-94.6859	Minnesota
MO	37.964253	-91.831833	Missouri
MS	32.354668	-89.398528	Mississippi
MT	46.879682	-110.362566	Montana
NC	35.759573	-79.0193	North Carolina
ND	47.551493	-101.002012	North Dakota
NE	41.492537	-99.901813	Nebraska
NH	43.193852	-71.572395	New Hampshire
NJ	40.058324	-74.405661	New Jersey
NM	34.97273	-105.032363	New Mexico
NV	38.80261	-116.419389	Nevada
NY	43.299428	-74.217933	New York
OH	40.417287	-82.907123	Ohio
OK	35.007752	-97.092877	Oklahoma
OR	43.804133	-120.554201	Oregon
PA	41.203322	-77.194525	Pennsylvania
PR	18.220833	-66.590149	Puerto Rico
RI	41.580095	-71.477429	Rhode Island
SC	33.836081	-81.163725	South Carolina
SD	43.969515	-99.901813	South Dakota
TN	35.517491	-86.580447	Tennessee
TX	31.968599	-99.901813	Texas
UT	39.32098	-111.093731	Utah
VA	37.431573	-78.656894	Virginia
VT	44.558803	-72.577841	Vermont
WA	47.751074	-120.740139	Washington
WI	43.78444	-88.787868	Wisconsin
WV	38.597626	-80.454903	West Virginia
WY	43.075968	-107.290284	Wyoming

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview

ERA5-Land hourly data from 1981 to present

Hong can only found Dec 1, 2020 data on Feb 19, 2021. 

2022-02-02. Hong found the title changed to "1950 to present", and resolution changed to 0.1 degree with a resolution about 9km. 

How to extract specific information from GRIB files?

https://gis.stackexchange.com/questions/173775/how-to-extract-specific-information-from-grib-files



https://github.com/jswhit/pygrib/issues/27

Coordinates, spatial grids
https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Spatialgrid

To find specific coordinates within this 720 x 360 array i just extrapolate the coordinates to the array. Ex: If I want to find data for New York New York coordinates ~40.5N 74 W I'll map those coordinates to the array to by doing 2x(90-40.5) and 2x (180-74) to find the following result:

grbs_old = pygrib.open('nomads.ncep.noaa.gov/cgi-bin/'+str(grib_filename))
data=grbs_old[6].values
data[99][212]

Grid box
https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step
Horizontal

In the horizontal the discrete points are arranged in a two dimensional grid and hence are referred to as grid points. The grid can be regular or irregular. An example of a regular latitude/longitude grid would be where the grid points are located every 1 degree of longitude in the east-west direction and every 1 degree of latitude in the north-south direction. Each grid point is associated with an area that either surrounds the grid point or lies between the grid points. This area is referred to as the "grid box".

Grid point values cannot properly represent variability on spatial scales smaller than the grid box.

(In addition to using grid points, the ECMWF Integrated Forecasting System (IFS) also uses an additional mathematical concept, spectral space, to represent horizontal space. This concept uses a set of wavy basis functions, spherical harmonics, to describe variations in the horizontal. The IFS switches between spectral space and grid point space, in order to perform specific computations.)

For Hong's Pygrib file,
grbs = pygrib.open('2019-2020June10.grib')
valuesTmp = grbs[1].values
valuesTmp.shape #return (721, 1440)

so, 720/180 = 4

1440/360 = 4

It turns out that GRIB use 0-360 longitude and many software use -180W, 180E longitude. 

Hong tried ( - 180W mod 360), and temperature results from DC matches AccuWeather results,

Python and GIS course

A student said that Python is used in GIS course. He was asked to review Python before taking the course.

GIS commons

http://giscommons.org/earth-and-map-preprocessing/

(todo) R GIS