Nishadh

Aspiring Science DevOps. @NORCAPweb Data Science @IGAD_CPAC on Impact Based Forecasting, Ex @SACONCoimbatore.

[Nishadh @nishadhka]

@mrocklin Best wishes for new role, coiled showed the power of dask and cloud computing for weather/climate data science.

[Nishadh @nishadhka]

@xarray_dev Wonderful, the seasonal drought analysis going to be very easy. Can extend https://t.co/yR9dLrlAbN

[Nishadh @nishadhka]

@MapMakinMeyers Thank you so much for this link, building footprint with height details is very essential for the flood impact based forecasting in identifying the vulnerability and exposure.

[Nishadh @nishadhka]

@danrothenberg @xarray_dev @CoiledHQ @cherian_deepak In hydrological modeling, to have average rainfall for sub basin levels, like on 1000s of small polygons, the xarray group_by operations have incredible application.

[naren.io @nareni0]

Okay I am the 90s kid who loved playing duck hunt in my NES console.

After I grew up I got my mind blown when I learnt how they developed the gun without any camera or motion sensors, leveraging only the CRT display. 🧵

[Lalit Patnaik @lalitpatnaik]

The best engineers are 'philosopher mechanics'.

[Nishadh @nishadhka]

@sean_a_mcclure Thank you, weather predictions and seeing it as an experiments would help in understanding its uncertainty

[Nahel Belgherze @WxNB_]

I’ve rarely ever seen such a high amount of available atmospheric moisture content over one of the driest regions on Earth. The anomalously northward-displaced ITF is resulting in a dramatic increase in precipitable water anomalies over the eastern Sahara, maxing out at ~9 sigma.

[Shruti Nath @ShrutiNath18]

Interested in research towards delivering actionable weather forecasts? In close collaboration with regional bodies we are developing AI-based rainfall forecasts for east Africa linking the chain from research to action.
https://t.co/0x59NBWWHT

@icpac_igad @UniofOxford

[TAMSAT @TAMSAT_Reading]

If you look closely, you can see that Nishadh is showing a TAMSAT-ALERT soil moisture forecast @UniRdg_Research @AtmosScience @NCEOscience

[Unix tool tip @UnixToolTip]

Seven habits of effective text editing
by Bram Moolenaar

https://t.co/U5kyFzoBkr

[Melanie Mitchell @MelMitchell1]

Very good piece on AI risk by @Jackstilgoe

https://t.co/XF6nnAwt6O

[Sandeep Mertia @SandeepMertia]

We're looking forward to hosting the Cultures of Datafication In & Across South Asia Workshop, April 12-13, @Penn.
Keynotes by @ishtiaqueSIA @neintara @actuarial_self @qadrida & Ursula Rao.
Reg. link: https://t.co/UFTFXLPH3l
Full program: https://t.co/60ZZ2mppnQ

[Complex Risk Analytics Fund @CrafdData]

#DataFinancing Excited to partner with @icpac_igad to improve climate resilience in Eastern Africa!

Our innovative approach to climate forecasting & disaster management aims to better protect communities from droughts, floods & other disasters.

https://t.co/C9rpb5SzUb

[Dr.Ahmed Amdihun @aamdihun]

Thrilled to be part of this great cohorts @icpac_igad

[Complex Risk Analytics Fund @CrafdData]

#NEW: $3.5M invested in data to transform crisis response!

CRAF'd & partners boost crisis #data by investing in projects with @IDMC_Geneva, @UNmigration, @icpac_igad, @RCClimate & @UNDP.

Together, we finance, connect, & reimagine data to save lives.

→https://t.co/6rkPYLhCUg

[Earthmover @EarthmoverHQ]

New blog post from @_jhamman : cloud-native data loaders for machine learning using @zarr_dev and @xarray_dev
https://t.co/b6xa9epHtP
1/6

[Nishadh @nishadhka]

@rahulatiitd https://t.co/tNJMcy4JzM

[Fermat's Library @fermatslibrary]

In the year 1961, the well-known meteorologist Edward Lorenz was engrossed in running a computational model for weather forecasting. Intent on restarting an interrupted simulation, he decided to utilize initial conditions from a prior printout, only to discover the ensuing simulation significantly deviating from the previous run.

The startling divergence was rooted in the subtle discrepancy of the printout values, rounded to a mere three decimal places, lacking the accuracy incorporated in the original calculations. Lorenz had anticipated that any deviations resulting from this loss in precision would be minor, however, they spiraled out of control. The errors escalated rapidly, doubling approximately every four days of simulated weather, and within the span of two simulated months, the weather scenario bore no resemblance to the initial conditions.

In this unexpected turn of events, Lorenz stumbled upon a phenomenon that would later be famously known as the butterfly effect.

This pivotal incident in the realm of chaos theory underscores the sensitivity of complex systems to minute changes in initial conditions. Its profound implications have rippled across diverse fields, from meteorology to finance to ecology, and beyond. The butterfly effect, originally identified in the world of weather simulations, serves as a profound reminder of the inherent unpredictability and complex interplay within natural systems.

This moment, forever ingrained in the annals of scientific exploration, marks a fundamental shift in understanding the dynamics of complex systems, echoing the consequences of minor alterations over time. It underscores the profound influence that seemingly insignificant changes can have, resulting in entirely different outcomes, forever altering our comprehension of the intricate interdependencies of the natural world.

[Prof. Anima Anandkumar @AnimaAnandkumar]

Thank you @ryanweather for posting about our FourCastNet model. It was the first AI high-resolution weather model to show that accurate weather forecasting is possible while being tens of thousands of times faster than numerical weather models, which paved the way for other AI models.
Access the model live giving real-time forecasts at @ECMWF https://t.co/sbRBbv9kqx
Our code and model are completely open-source with an Apache license. It’s very easy to get started. Use the model from @ECMWF https://t.co/Oc65qvuXpJ and https://t.co/ImAY7uPxFD for code to train the model