Seminar at the U. Washington. The Climate Corporation (“TCC”) makes decision-making tools for farmers. 24-hour quantitative precipitation estimates (QPEs) drive a number of farming decisions, and the accuracy of the 24h QPEs in turn affect the accuracy of TCC's agronomic models. Therefore, the creation of accurate QPEs is an area of active research focus at TCC. In this talk, I talk about the multi-sensor QPE pipeline, how we estimate and show uncertainty and how we evaluate the performance of QPE algorithms on farming use cases. In addition, TCC is conducting field experiments at several locations throughout the Midwest. I will present findings related to the intra-field variability of precipitation.

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QPE at the Climate Corporation
lak@climate.com www.vlakshman.com
Talk at U. Washington, Jan 28, 2016
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The Climate Corporation (TCC) provides
decision-making tools for farmers
Weather is both a feature that we provide to our users, and an input to our agronomic models
http://www.climate.com/

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24h rainfall estimates are critical to many
farming decisions and advisors
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Crop health/damage Field workability
If and when to fertilize

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Agronomic models are sensitive to the accuracy
of the rainfall estimates
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lbs/acre of
fertilizer
MAE of QPE (mm)

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Our team in 2015
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Scientists, engineers, statisticians, and data specialists
work together to curate comprehensive data sets and
develop scalable, production-ready algorithms.

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Our team in 2015
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Scientists, engineers, statisticians, and data specialists
work together to curate comprehensive data sets and
develop scalable, production-ready algorithms.
Raining
Champ

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Our QPE pipeline has uncertainty estimation,
but is otherwise similar to, say, AHPS
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Drop Size Distribution (DSD) variability induces
uncertainty in radar QPE.
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R(A)
R(Z)
R(Kdp)
Fig 1 of Ryzhkov 2014 variance from 47k DSDs
Fall speed vs drop size
Bayesian sampling of DSD and rain rate from dual-pol radar
observations.
Probabilistic QPE
Latent DSD
Source: TCC Patent GENERATING PROBABILISTIC ESTIMATES
OF RAINFALL RATES FROM RADAR REFLECTIVITY
MEASUREMENTS by Kleeman, Lakshmanan, Reid

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We optimize every step of the pipeline, and often
end up creating new/hybrid approaches
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Quantified differences between:
● QPE methods
● mosaicing approaches
● gauge fusion methods
● other “minor” parts of pipeline
As we implemented and measured
known approaches, we also ended
up implementing new/hybrid
methods that outperformed what’s
known in the literature

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This is an example of one such comparison, on
the use cases that matter to our customers
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There is of course a computational constraint to
be able to do this quickly and routinely
http://goo.gl/NB5hDO

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So, we do almost everything on the cloud
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https://cloud.google.com/
https://aws.amazon.com/
Elastic
Compute Cloud
(EC2)
Simple Storage
Service (S3)

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The NOAA CRADA puts NEXRAD data on S3,
thus making our science faster and better
Simple Storage
Service (S3)
1. Launch
Elastic Compute
Cloud (EC2)
Amazon public bucket
2. Compute

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There is a limit to what you can do with radar
About 20% of our users live more than 150km away from a NEXRAD, so the solution will also
involve deploying weather stations in an optimal manner
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We instrumented Climate Research Farms to
help explain spatial variability of rainfall
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These are truly independent gauges, so we also
used them to evaluate QPE performance
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small
data!