The nature of rain events in summer vs. winter at the SGP ARM Facility MPO 581 Class Project Emily Riley, Siwon Song, & Brian Mapes
Background ARM – Atmospheric Radiation Measurements – Several Sites (facilities) funded by DOE
Background ARM – Atmospheric Radiation Measurements – SGP-Southern Great Plains
Data Climate Modeling Best Estimate (CMBE) Data – 1996 – 2009 – Data averaged over one hour time intervals
Data Climate Modeling Best Estimate (CMBE) Data – 1996 – 2009 – Data averaged over one hour time intervals * Cloud fraction profiles * Total, high, middle, and low clouds * Liquid water path and precipitable water vapor * Surface radiative fluxes * TOA radiative fluxes * Soundings * NWP analysis data * Surface sensible and latent heat fluxes * Surface precipitation * Surface temperature, relative humidity, and horizontal winds
Methods Composite Weighted Composite Regression
July 1996 – RH and Precipitation Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t) : Rain Event
non-weighted composite method non-weighted composite t lag = [-7*24, 7*24] hours N = total number of selected rain events
non-weighted composite -7 days+7 days Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t) -7 days+7 days-7 days+7 days-7 days+7 days-7 days+7 days-7 days+7 days : Rain Event
Weighted composite method weighted composite t lag = [-7*24, 7*24] hours N = total number of selected rain events Non-weighting
Weighted composite -7 days+7 days Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t) -7 days+7 days-7 days+7 days-7 days+7 days-7 days+7 days-7 days+7 days : Rain Event
Composite vs. Weighted Composite mm/hr
Regression method Regression Coefficient t = whole time series t lag = [-7*24, 7*24] hours
Regression method: t = 0 hour Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t)
Regression method: t = -10 hour Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t)
Regression method: t = +10 hour Relative Humidity (RH) at surface – ψ(t) Days on July 1996 [mm/hr] [%] Precipitation Rate at surface – p(t)
Comparison: Regression vs. weighted composite Regression Coefficient Weighted composite t = whole time series t lag = [-7*24, 7*24] hours N = total number of selected rain events
Weighted Composite vs. Regression % RH on precipitation
Weighted Composite vs. Regression SAME, except for units % RH on precipitation
Relative Humidity Perturbation Weighted [%] Regression [%/(mm/hr)] Non-weighted [%]
Time for some results…. Oklahoma JJA vs. DJF precipitation
Cumulative Fraction of Rain Events JJA DJF DJF - ~45% time not raining JJA - ~32% time not raining
Cumulative Fraction of Rain Events JJA DJF DJF - ~45% time not raining JJA - ~32% time not raining 5 ~20% rain events > 5 mm/hr
Cumulative Fraction of Rain Events JJA DJF DJF - ~45% time not raining JJA - ~32% time not raining 5 ~5% rain events > 5 mm/hr
Summer vs. Winter Precipitation
Summer vs. Winter Temperature Strong Diurnal Cycle Weak Diurnal Cycle
Summer vs. Winter Temperature Strong Diurnal Cycle Weak Diurnal Cycle Frontal Precip Afternoon Convection
Seasonal: Temperature perturbation [K] DJF: Winter JJA: Summer
Summer vs. Winter Surface Pressure
Summer vs. Winter Relative Humidity
Seasonal: Relative Humidity perturbation [%] DJF: Winter JJA: Summer
Seasonal: Relative Humidity perturbation [%] DJF: Winter JJA: Summer
Summer vs. Winter LWP
Summer vs. Winter Cloud Fraction
Seasonal: All Cloud Fraction perturbation [%] DJF: Winter JJA: Summer
Summer vs. Winter Cloud Top Height
Seasonal: Omega perturbation [Pa/s] DJF: Winter JJA: Summer
Summary Summer (JJA): – More rain events – Heavier, but shorter rain events – Stronger diurnal cycle – Higher cloud tops Winter (DJF): – Tilted vertical structure for RH and Cloud Fraction THOUGH, hourly time composites might reveal tilted structure in summer